{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import re\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import SnowballStemmer\n",
    "import pymongo\n",
    "import os\n",
    "stop = stopwords.words('english')\n",
    "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\n",
    "from tqdm import tqdm\n",
    "import joblib\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "import string\n",
    "# df = pd.read_csv('../data/version2.csv',encoding = 'ISO-8859-1')\n",
    "# # cols = [col for col in df if not col.startswith('Unnamed:')]\n",
    "# # df = df[cols]\n",
    "# print(df)\n",
    "# b_set = df\n",
    "# b_set['SUBJpolit'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "\n",
    "import warnings\n",
    "import joblib\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████| 800/800 [00:01<00:00, 600.60it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                               Sentence  polit\n",
      "0     emojipediaapple android io user able choose ne...      1\n",
      "1     both google apple revealed version new emoji o...      1\n",
      "2     the new emoji come googles android apples io l...      1\n",
      "3     include new animal like bison beaver polar bea...      1\n",
      "4     youll also find slightly smiling face two peop...      1\n",
      "...                                                 ...    ...\n",
      "5003  the number daily coronavirus test pennsylvania...      2\n",
      "5004  that break to case death lehigh county new cas...      2\n",
      "5005  case death northampton county new case two dea...      2\n",
      "5006    the county averaged new case day last seven day      2\n",
      "5007  more death also reported county border lehigh ...      2\n",
      "\n",
      "[5008 rows x 2 columns]\n",
      "                                               Sentence  polit\n",
      "1794  these pattern even extend decade trend longter...      1\n",
      "1677  even though its painful times its rewarding se...      1\n",
      "3528  now would find brittle defensive selfexclamato...      2\n",
      "254   a state prosecutor involved case herwig schäfe...      1\n",
      "3167  drew mcintyre given former tagteam partner dol...      2\n",
      "...                                                 ...    ...\n",
      "572   jaden willow performance wireless fest london ...      1\n",
      "1729  new research outline potential strategy detect...      1\n",
      "207   human activity data available three source for...      1\n",
      "1592  i still much hope i back first special were go...      1\n",
      "1348  i grew surrounded men cared wore brook brother...      1\n",
      "\n",
      "[5008 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "myclient = pymongo.MongoClient(\"mongodb://localhost:27017/\")\n",
    "mydb = myclient[\"news\"]\n",
    "mycol = mydb[\"political\"]\n",
    "left_query = { \"nutrition.political_bias.subfeatures.value\": 0}\n",
    "right_query = { \"nutrition.political_bias.subfeatures.value\": 4}\n",
    "\n",
    "lr = mycol.find(left_query).limit(400)\n",
    "rr = mycol.find(right_query).limit(400)\n",
    "left_data = pd.DataFrame(columns=['Sentence'],data=[i['article']['text'] for i in list(lr)] )\n",
    "right_data = pd.DataFrame(columns=['Sentence'],data=[i['article']['text'] for i in list(rr)] )\n",
    "left_data['polit'] = 1\n",
    "right_data['polit'] = 2\n",
    "pc = r\"\\d+\\.?\\d*\"\n",
    "regex = re.compile('[%s]' % re.escape(string.punctuation+'”“’�—–≥≤€‘…°'))\n",
    "stop = stopwords.words('english')\n",
    "c_set = pd.concat( [left_data,right_data], axis=0,ignore_index=True)\n",
    "d_set = pd.concat( [left_data,right_data], axis=0,ignore_index=True)\n",
    "d_set.drop(d_set.index, inplace=True)\n",
    "for (index,item) in enumerate(tqdm(c_set['Sentence'])):\n",
    "    tmp = pd.DataFrame(columns=['Sentence','polit'],data=[(x.strip() ,c_set['polit'][index]) \n",
    "                                                          for x in item.split('.') if len(x.strip())>50 and len(x)<140] )\n",
    "    d_set = pd.concat([d_set,tmp], axis=0,ignore_index=True)\n",
    "    \n",
    "d_set['Sentence'] = d_set['Sentence'].apply(lambda w:' '.join([regex.sub('',re.sub(pc,'',' '.join\n",
    "                                                                                   (WordNetLemmatizer().lemmatize(word.lower()).split()))) \n",
    "                                                               for word in str(w).split() if word not in (stop)]))\n",
    "d_set.to_csv('../data/merged_test.csv',encoding='utf_8_sig',index=None)\n",
    "\n",
    "d_set = pd.DataFrame(columns=['Sentence','polit'],data=[(' '.join\n",
    "                                                         (item.split()),d_set[d_set['Sentence'] == item]['polit'].values[0]) \n",
    "                                                        for item in d_set['Sentence'] if (len(item.split(' '))>10 \n",
    "                                                                                          and len(item.split(' '))<20) ] )\n",
    "# c_set = c_set.sample(frac = 1)\n",
    "print(d_set)\n",
    "d_set.to_csv('../data/merged_test_ver2.csv',encoding='utf_8_sig',index=None)\n",
    "d_set = d_set.sample(frac = 1)\n",
    "print(d_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                               Sentence  polit\n",
      "0     emojipediaapple android io user able choose ne...      1\n",
      "1     both google apple revealed version new emoji o...      1\n",
      "2     the new emoji come googles android apples io l...      1\n",
      "3     include new animal like bison beaver polar bea...      1\n",
      "4     youll also find slightly smiling face two peop...      1\n",
      "...                                                 ...    ...\n",
      "5003  the number daily coronavirus test pennsylvania...      2\n",
      "5004  that break to case death lehigh county new cas...      2\n",
      "5005  case death northampton county new case two dea...      2\n",
      "5006    the county averaged new case day last seven day      2\n",
      "5007  more death also reported county border lehigh ...      2\n",
      "\n",
      "[5008 rows x 2 columns]\n",
      "Train set shape: (5008, 2)\n",
      "1    2735\n",
      "2    2273\n",
      "Name: polit, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('../data/merged_test_ver2.csv')\n",
    "# cols = [col for col in df if not col.startswith('Unnamed:')]\n",
    "# df = df[cols]\n",
    "print(df)\n",
    "d_set = df\n",
    "d_set['polit'].value_counts()\n",
    "\n",
    "print(\"Train set shape:\", d_set.shape)\n",
    "print(d_set['polit'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.bias', 'cls.seq_relationship.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.dense.weight', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       [101, 7861, 29147, 15457, 9032, 23804, 11924, ...\n",
      "1       [101, 2119, 8224, 6207, 3936, 2544, 2047, 7861...\n",
      "2       [101, 1996, 2047, 7861, 29147, 2072, 2272, 822...\n",
      "3       [101, 2421, 2047, 4111, 2066, 22285, 13570, 11...\n",
      "4       [101, 2017, 3363, 2036, 2424, 3621, 5629, 2227...\n",
      "                              ...                        \n",
      "5003    [101, 1996, 2193, 3679, 21887, 23350, 3231, 35...\n",
      "5004    [101, 2008, 3338, 2000, 2553, 2331, 23241, 222...\n",
      "5005    [101, 2553, 2331, 15944, 2221, 2047, 2553, 204...\n",
      "5006    [101, 1996, 2221, 11398, 2047, 2553, 2154, 219...\n",
      "5007    [101, 2062, 2331, 2036, 2988, 2221, 3675, 2324...\n",
      "Name: Sentence, Length: 5008, dtype: object\n",
      "train set shape: (5008, 36)\n",
      "torch.Size([5008, 36, 768])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import transformers as tfs\n",
    "# read model from lib\n",
    "model_class, tokenizer_class, pretrained_weights = (tfs.BertModel, tfs.BertTokenizer, 'bert-base-uncased')\n",
    "tokenizer = tokenizer_class.from_pretrained(pretrained_weights)\n",
    "model = model_class.from_pretrained(pretrained_weights)\n",
    "# Means to add [CLS] and [SEP] symbols at the beginning and end of the sentence\n",
    "train_tokenized = d_set['Sentence'].apply((lambda x: tokenizer.encode(x, add_special_tokens=True,max_length=77,truncation=True)))\n",
    "print(train_tokenized)\n",
    "#process the sentences into the same length\n",
    "train_max_len = 0\n",
    "for i in train_tokenized.values:\n",
    "    if len(i) > train_max_len:\n",
    "        train_max_len = len(i)\n",
    "train_padded = np.array([i + [0] * (train_max_len-len(i)) for i in train_tokenized.values]) #add 0 sufix\n",
    "print(\"train set shape:\",train_padded.shape)\n",
    "#let the model know which words are not to be processed \"the [PAD] symbol\"\n",
    "train_attention_mask = np.where(train_padded != 0, 1, 0)\n",
    "#in order to improve the trv aining speed, add a series of [PAD] symbols at the end of short sentences:\n",
    "train_input_ids = torch.tensor(train_padded).long()\n",
    "train_attention_mask = torch.tensor(train_attention_mask).long()\n",
    "with torch.no_grad():\n",
    "    train_last_hidden_states = model(train_input_ids, attention_mask=train_attention_mask)\n",
    "print(train_last_hidden_states[0].size())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2]\n",
      "[0.00596469 0.0157681  0.0218933  ... 0.79921201 0.97314752 0.96009494]\n",
      "[1 1 1 ... 2 2 2]\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import make_classification     \n",
    "from sklearn.linear_model import LogisticRegression  \n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.metrics import roc_curve                \n",
    "from sklearn.metrics import precision_recall_curve   \n",
    "from sklearn.metrics import f1_score                 \n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "import joblib\n",
    "training_accuracy = []\n",
    "test_accuracy = []\n",
    "# train_last_hid den_states = joblib.load('../models/6370train_last_hidden_states.b')\n",
    "lr_clf = joblib.load('../models/6370lr_clf.clf')\n",
    "print(lr_clf.classes_)\n",
    "train_features = train_last_hidden_states[0][:,0,:].numpy()\n",
    "# train_labels = b_set['SUBJpolit']\n",
    "# lr_clf = LogisticRegression(max_iter=10000,class_weight=\"balanced\")\n",
    "# lr_clf.fit(X_train, Y_train)\n",
    "y_pred_prob = lr_clf.predict_proba(train_features)\n",
    "\n",
    "# joblib.dump(lr_clf,'../models/6370lr_clf.clf')\n",
    "# y_pred_prob = svm_clf.predict_proba(X_test)\n",
    "y_pred = y_pred_prob[:, 1]\n",
    "print(y_pred)\n",
    "y_pred_prob = (y_pred > 0.5).astype('int')+1\n",
    "print(y_pred_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sentence</th>\n",
       "      <th>polit</th>\n",
       "      <th>prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emojipediaapple android io user able choose ne...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.994035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>both google apple revealed version new emoji o...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.984232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>the new emoji come googles android apples io l...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.978107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>include new animal like bison beaver polar bea...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.988891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>youll also find slightly smiling face two peop...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.914570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5003</th>\n",
       "      <td>the number daily coronavirus test pennsylvania...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.990859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5004</th>\n",
       "      <td>that break to case death lehigh county new cas...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.744734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5005</th>\n",
       "      <td>case death northampton county new case two dea...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.799212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5006</th>\n",
       "      <td>the county averaged new case day last seven day</td>\n",
       "      <td>2</td>\n",
       "      <td>0.973148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5007</th>\n",
       "      <td>more death also reported county border lehigh ...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.960095</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5008 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Sentence  polit      prob\n",
       "0     emojipediaapple android io user able choose ne...      1  0.994035\n",
       "1     both google apple revealed version new emoji o...      1  0.984232\n",
       "2     the new emoji come googles android apples io l...      1  0.978107\n",
       "3     include new animal like bison beaver polar bea...      1  0.988891\n",
       "4     youll also find slightly smiling face two peop...      1  0.914570\n",
       "...                                                 ...    ...       ...\n",
       "5003  the number daily coronavirus test pennsylvania...      2  0.990859\n",
       "5004  that break to case death lehigh county new cas...      2  0.744734\n",
       "5005  case death northampton county new case two dea...      2  0.799212\n",
       "5006    the county averaged new case day last seven day      2  0.973148\n",
       "5007  more death also reported county border lehigh ...      2  0.960095\n",
       "\n",
       "[5008 rows x 3 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "de = train_last_hidden_states[0][:,0,:].numpy()\n",
    "probs = pd.DataFrame(de.max(axis=1),columns=['prob'])\n",
    "hs = pd.concat((d_set,probs),axis=1)\n",
    "hs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(b_set['polit'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sentence</th>\n",
       "      <th>polit</th>\n",
       "      <th>prob</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emojipediaapple android io user able choose ne...</td>\n",
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       "    <tr>\n",
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       "      <td>[-0.2784818708896637, -0.049149200320243835, 0...</td>\n",
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       "      <th>2</th>\n",
       "      <td>the new emoji come googles android apples io l...</td>\n",
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       "      <td>[-0.6285604238510132, -0.20750272274017334, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>include new animal like bison beaver polar bea...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.988891</td>\n",
       "      <td>[-0.45708131790161133, -0.14758947491645813, 0...</td>\n",
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       "      <td>youll also find slightly smiling face two peop...</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5003</th>\n",
       "      <td>the number daily coronavirus test pennsylvania...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.990859</td>\n",
       "      <td>[-0.3218143880367279, 0.008096681907773018, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5004</th>\n",
       "      <td>that break to case death lehigh county new cas...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.744734</td>\n",
       "      <td>[-0.33142003417015076, -0.25465643405914307, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5005</th>\n",
       "      <td>case death northampton county new case two dea...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.799212</td>\n",
       "      <td>[-0.38500604033470154, -0.24400034546852112, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5006</th>\n",
       "      <td>the county averaged new case day last seven day</td>\n",
       "      <td>2</td>\n",
       "      <td>0.973148</td>\n",
       "      <td>[-0.054275937378406525, -0.005925729870796204,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5007</th>\n",
       "      <td>more death also reported county border lehigh ...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.960095</td>\n",
       "      <td>[-0.6338779926300049, -0.032490938901901245, 0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5008 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Sentence  polit      prob  \\\n",
       "0     emojipediaapple android io user able choose ne...      1  0.994035   \n",
       "1     both google apple revealed version new emoji o...      1  0.984232   \n",
       "2     the new emoji come googles android apples io l...      1  0.978107   \n",
       "3     include new animal like bison beaver polar bea...      1  0.988891   \n",
       "4     youll also find slightly smiling face two peop...      1  0.914570   \n",
       "...                                                 ...    ...       ...   \n",
       "5003  the number daily coronavirus test pennsylvania...      2  0.990859   \n",
       "5004  that break to case death lehigh county new cas...      2  0.744734   \n",
       "5005  case death northampton county new case two dea...      2  0.799212   \n",
       "5006    the county averaged new case day last seven day      2  0.973148   \n",
       "5007  more death also reported county border lehigh ...      2  0.960095   \n",
       "\n",
       "                                               features  \n",
       "0     [-0.4873981177806854, -0.3077705502510071, 0.1...  \n",
       "1     [-0.2784818708896637, -0.049149200320243835, 0...  \n",
       "2     [-0.6285604238510132, -0.20750272274017334, 0....  \n",
       "3     [-0.45708131790161133, -0.14758947491645813, 0...  \n",
       "4     [-0.1692407727241516, 0.0050483886152505875, 0...  \n",
       "...                                                 ...  \n",
       "5003  [-0.3218143880367279, 0.008096681907773018, 0....  \n",
       "5004  [-0.33142003417015076, -0.25465643405914307, -...  \n",
       "5005  [-0.38500604033470154, -0.24400034546852112, 0...  \n",
       "5006  [-0.054275937378406525, -0.005925729870796204,...  \n",
       "5007  [-0.6338779926300049, -0.032490938901901245, 0...  \n",
       "\n",
       "[5008 rows x 4 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf = train_last_hidden_states[0][:,0,:].numpy()\n",
    "hs['features'] = tf.tolist()\n",
    "hs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sentence</th>\n",
       "      <th>polit</th>\n",
       "      <th>prob</th>\n",
       "      <th>features</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>emojipediaapple android io user able choose ne...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.994035</td>\n",
       "      <td>[-0.4873981177806854, -0.3077705502510071, 0.1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>both google apple revealed version new emoji o...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.984232</td>\n",
       "      <td>[-0.2784818708896637, -0.049149200320243835, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>the new emoji come googles android apples io l...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.978107</td>\n",
       "      <td>[-0.6285604238510132, -0.20750272274017334, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>include new animal like bison beaver polar bea...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.988891</td>\n",
       "      <td>[-0.45708131790161133, -0.14758947491645813, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>youll also find slightly smiling face two peop...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.914570</td>\n",
       "      <td>[-0.1692407727241516, 0.0050483886152505875, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5003</th>\n",
       "      <td>the number daily coronavirus test pennsylvania...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.990859</td>\n",
       "      <td>[-0.3218143880367279, 0.008096681907773018, 0....</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5004</th>\n",
       "      <td>that break to case death lehigh county new cas...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.744734</td>\n",
       "      <td>[-0.33142003417015076, -0.25465643405914307, -...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5005</th>\n",
       "      <td>case death northampton county new case two dea...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.799212</td>\n",
       "      <td>[-0.38500604033470154, -0.24400034546852112, 0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5006</th>\n",
       "      <td>the county averaged new case day last seven day</td>\n",
       "      <td>2</td>\n",
       "      <td>0.973148</td>\n",
       "      <td>[-0.054275937378406525, -0.005925729870796204,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5007</th>\n",
       "      <td>more death also reported county border lehigh ...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.960095</td>\n",
       "      <td>[-0.6338779926300049, -0.032490938901901245, 0...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3978 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Sentence  polit      prob  \\\n",
       "0     emojipediaapple android io user able choose ne...      1  0.994035   \n",
       "1     both google apple revealed version new emoji o...      1  0.984232   \n",
       "2     the new emoji come googles android apples io l...      1  0.978107   \n",
       "3     include new animal like bison beaver polar bea...      1  0.988891   \n",
       "4     youll also find slightly smiling face two peop...      1  0.914570   \n",
       "...                                                 ...    ...       ...   \n",
       "5003  the number daily coronavirus test pennsylvania...      2  0.990859   \n",
       "5004  that break to case death lehigh county new cas...      2  0.744734   \n",
       "5005  case death northampton county new case two dea...      2  0.799212   \n",
       "5006    the county averaged new case day last seven day      2  0.973148   \n",
       "5007  more death also reported county border lehigh ...      2  0.960095   \n",
       "\n",
       "                                               features  \n",
       "0     [-0.4873981177806854, -0.3077705502510071, 0.1...  \n",
       "1     [-0.2784818708896637, -0.049149200320243835, 0...  \n",
       "2     [-0.6285604238510132, -0.20750272274017334, 0....  \n",
       "3     [-0.45708131790161133, -0.14758947491645813, 0...  \n",
       "4     [-0.1692407727241516, 0.0050483886152505875, 0...  \n",
       "...                                                 ...  \n",
       "5003  [-0.3218143880367279, 0.008096681907773018, 0....  \n",
       "5004  [-0.33142003417015076, -0.25465643405914307, -...  \n",
       "5005  [-0.38500604033470154, -0.24400034546852112, 0...  \n",
       "5006  [-0.054275937378406525, -0.005925729870796204,...  \n",
       "5007  [-0.6338779926300049, -0.032490938901901245, 0...  \n",
       "\n",
       "[3978 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs1 = hs[hs['prob']>0.7]\n",
    "hs1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Sentence', 'polit']\n"
     ]
    }
   ],
   "source": [
    "col_name=d_set.columns.tolist()                   \n",
    "print(col_name)\n",
    "col_name.insert(2,'new_polit')                      \n",
    "d_set=d_set.reindex(columns=col_name)              \n",
    "d_set['new_polit']=y_pred_prob   \n",
    "d_set.to_csv('../data/temp/tmp3.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                               Sentence  polit  new_polit\n",
      "3528  now would find brittle defensive selfexclamato...      2          2\n",
      "254   a state prosecutor involved case herwig schäfe...      1          2\n",
      "4891  the irs payment plan available taxpayer cant a...      2          2\n",
      "3262  on thursday day average daily new coronavirus ...      2          2\n",
      "204   it wasnt even april year nasa able put enormou...      1          2\n",
      "...                                                 ...    ...        ...\n",
      "2713  i think i got entire state texas nevada said l...      1          2\n",
      "2921  recent ruling issue could cause conservative p...      2          2\n",
      "2851  in words past seven days average nearly one te...      2          2\n",
      "3716  an easter sunrise service red rock amphitheatr...      2          2\n",
      "4942  when practical law enforcement issue arise tri...      2          2\n",
      "\n",
      "[1988 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "# clear the dataset\n",
    "# d_set.drop('new_polit',axis = 1,inplace = True)  # Delete the column, the returned new array is replaced and saved in a\n",
    "e_set = d_set[d_set['new_polit']==2]\n",
    "e_set['polit'].value_counts()\n",
    "print(e_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of the model checkpoint at bert-base-uncased were not used when initializing BertModel: ['cls.predictions.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.seq_relationship.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.decoder.weight', 'cls.predictions.transform.dense.bias', 'cls.seq_relationship.weight']\n",
      "- This IS expected if you are initializing BertModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3528    [101, 2085, 2052, 2424, 24650, 5600, 2969, 102...\n",
      "254     [101, 1037, 2110, 12478, 2920, 2553, 2014, 162...\n",
      "4891    [101, 1996, 25760, 7909, 2933, 2800, 26980, 20...\n",
      "3262    [101, 2006, 9432, 2154, 2779, 3679, 2047, 2188...\n",
      "204     [101, 2009, 2347, 2102, 2130, 2258, 2095, 9274...\n",
      "                              ...                        \n",
      "2713    [101, 1045, 2228, 1045, 2288, 2972, 2110, 3146...\n",
      "2921    [101, 3522, 6996, 3277, 2071, 3426, 4603, 5245...\n",
      "2851    [101, 1999, 2616, 2627, 2698, 2420, 2779, 3053...\n",
      "3716    [101, 2019, 10957, 13932, 2326, 2417, 2600, 23...\n",
      "4942    [101, 2043, 6742, 2375, 7285, 3277, 13368, 591...\n",
      "Name: Sentence, Length: 1988, dtype: object\n",
      "train set shape: (1988, 31)\n",
      "torch.Size([1988, 31, 768])\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import transformers as tfs\n",
    "# read model from lib\n",
    "model_class, tokenizer_class, pretrained_weights = (tfs.BertModel, tfs.BertTokenizer, 'bert-base-uncased')\n",
    "tokenizer = tokenizer_class.from_pretrained(pretrained_weights)\n",
    "model = model_class.from_pretrained(pretrained_weights)\n",
    "# Means to add [CLS] and [SEP] symbols at the beginning and end of the sentence\n",
    "train_tokenized = e_set['Sentence'].apply((lambda x: tokenizer.encode(x, add_special_tokens=True,max_length=77,truncation=True)))\n",
    "print(train_tokenized)\n",
    "#process the sentences into the same length\n",
    "train_max_len = 0\n",
    "for i in train_tokenized.values:\n",
    "    if len(i) > train_max_len:\n",
    "        train_max_len = len(i)\n",
    "train_padded = np.array([i + [0] * (train_max_len-len(i)) for i in train_tokenized.values]) #add 0 sufix\n",
    "print(\"train set shape:\",train_padded.shape)\n",
    "\n",
    "#output：train set shape: (3000, 66)\n",
    "#let the model know which words are not to be processed \"the [PAD] symbol\"\n",
    "train_attention_mask = np.where(train_padded != 0, 1, 0)\n",
    "#in order to improve the training speed, add a series of [PAD] symbols at the end of short sentences:\n",
    "train_input_ids = torch.tensor(train_padded).long()\n",
    "train_attention_mask = torch.tensor(train_attention_mask).long()\n",
    "with torch.no_grad():\n",
    "    train_last_hidden_states = model(train_input_ids, attention_mask=train_attention_mask)\n",
    "print(train_last_hidden_states[0].size())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5008"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(class_weight='balanced', max_iter=10000)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import ComplementNB\n",
    "from sklearn.naive_bayes import BernoulliNB \n",
    "from sklearn.naive_bayes import CategoricalNB\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import roc_curve                \n",
    "from sklearn.metrics import recall_score   \n",
    "from sklearn.metrics import f1_score                 \n",
    "from sklearn.datasets import make_classification    \n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "import joblib\n",
    "from nltk.stem.wordnet import WordNetLemmatizer\n",
    "\n",
    "gnb = GaussianNB()\n",
    "bnb = BernoulliNB()\n",
    "# train_last_hidden_states = joblib.load('../models/4251_clear.bert')\n",
    "# b_set = joblib.load('../models/4251_clear.data')\n",
    "# b_set['Sentence'] = b_set['Sentence'].apply(lambda w:' '.join([WordNetLemmatizer().lemmatize(word.lower()) for word in str(w).split()]))\n",
    "\n",
    "train_features = train_last_hidden_states[0][:,0,:].numpy()\n",
    "train_labels = e_set['polit']\n",
    "lr_clf = LogisticRegression(max_iter=10000,class_weight=\"balanced\")\n",
    "svm_clf = SVC(C=0.8, kernel='linear', gamma=20, decision_function_shape='ovr',class_weight=\"balanced\",probability=True)\n",
    "thresset = [0.4,0.5,0.6,0.7,0.8]\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(train_features,train_labels.astype('int'),test_size=0.3, random_state=3)\n",
    "\n",
    "gnb_clf = gnb.fit(X_train, Y_train)\n",
    "bnb_clf = bnb.fit(X_train, Y_train)\n",
    "svm_clf.fit(X_train, Y_train.ravel())\n",
    "lr_clf.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00,  5.20it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "aver = 'weighted'\n",
    "training_accuracy = []\n",
    "gnb_test_accuracy = []\n",
    "gnb_test_recall = []\n",
    "gnb_test_fscore = []\n",
    "gnb_drop_item = []\n",
    "bnb_test_accuracy = []\n",
    "bnb_test_recall = []\n",
    "bnb_test_fscore = []\n",
    "bnb_drop_item = []\n",
    "svm_test_accuracy = []\n",
    "svm_test_recall = []\n",
    "svm_test_fscore = []\n",
    "svm_drop_item = []\n",
    "lr_test_accuracy =[]\n",
    "lr_test_recall =[]\n",
    "lr_test_fscore =[]\n",
    "lr_drop_item = []\n",
    "for i in tqdm(thresset):\n",
    "# i=0.8\n",
    "    y_pred = svm_clf.predict_proba(X_test)\n",
    "    y_pred = y_pred[:, 1]\n",
    "    y_pred_prob = (y_pred >  i).astype('int')+1\n",
    "    svm_acu = precision_score(Y_test, y_pred_prob,average=aver)\n",
    "    svm_recall = recall_score(Y_test, y_pred_prob,average=aver)\n",
    "    svm_fscore = f1_score(Y_test, y_pred_prob,average=aver)\n",
    "    svm_test_accuracy.append(svm_acu)\n",
    "    svm_test_recall.append(svm_recall)\n",
    "    svm_test_fscore.append(svm_fscore)\n",
    "    svm_drop_item.append(list((y_pred>i).astype('int')).count(0))\n",
    "    y_pred2 = lr_clf.predict_proba(X_test)\n",
    "    y_pred2 = y_pred2[:, 1]\n",
    "    y_pred_prob2 = (y_pred2 > i).astype('int')+1\n",
    "    lr_accuracy = precision_score(Y_test, y_pred_prob2,average=aver)\n",
    "    lr_recall = recall_score(Y_test, y_pred_prob2,average=aver)\n",
    "    lr_fscore = f1_score(Y_test, y_pred_prob2,average=aver)\n",
    "    lr_test_accuracy.append(lr_accuracy)\n",
    "    lr_test_recall.append(lr_recall)\n",
    "    lr_test_fscore.append(lr_fscore)\n",
    "    lr_drop_item.append(list((y_pred2>i).astype('int')).count(0))\n",
    "\n",
    "    y_pred1 = gnb_clf.predict_proba(X_test)\n",
    "    y_pred1 = y_pred1[:, 1]\n",
    "    y_pred1_prob = (y_pred1 >  i).astype('int')+1\n",
    "    y_pred4 = bnb_clf.predict_proba(X_test)\n",
    "    y_pred4 = y_pred4[:, 1]\n",
    "    y_pred4_prob = (y_pred4 >  i).astype('int')+1\n",
    "    gnb_test_accuracy.append(precision_score(Y_test,y_pred1_prob,average=aver))\n",
    "    gnb_test_recall.append(recall_score(Y_test,y_pred1_prob,average=aver))\n",
    "    gnb_test_fscore.append(f1_score(Y_test,y_pred1_prob,average=aver))\n",
    "    gnb_drop_item.append(list((y_pred1>i).astype('int')).count(0))\n",
    "\n",
    "    bnb_test_recall.append(recall_score(Y_test,y_pred4_prob,average=aver))\n",
    "    bnb_test_fscore.append(f1_score(Y_test,y_pred4_prob,average=aver))\n",
    "    bnb_test_accuracy.append(precision_score(Y_test,y_pred4_prob,average=aver))\n",
    "    bnb_drop_item.append(list((y_pred4>i).astype('int')).count(0))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jMvjJoxY5IniLC93rhBF90230ajZQL3xUVYomCKXKW3ICGfu+Z/PB/yMmdR8barly2tVqXbR29ye6YS+iW/+BDvUi9darqlLTBKGUI+VkYY7+yP74r4k5voGYnN+I9ahFrgg+uNCjTmvujnyUzk37OTtSpa6gCUKpinTuDGl7l7D5wGJiUuKIcbOR6mJjatuxDOg83tnRKVWIJgilnCUvj/SENYxb9xS7bHm8UbcXQ259F/Se3KqSuFaCqIS3HVOqGrHZ8GnRn/f/uIIIVz8mnt3Ad/8YAOmnnB2ZUtelCUKpCuBduz5z7l5BZ59mvGTO8s2iPnBwtbPDUuqaHJogRGSIiOwVkQMi8sJVyvQVkVgR2S0i60pTV6mqxMvNi9m3f0X3upG86uvOF9/+CVZNsW6OpFQl5LAEISIuwLvArUA4cK+IhBcpUwd4DxhqjGkL3FXSukpVRR6uHswa8iG9G/ZkSlAAn+yYCx/dBqnHr19ZqQrmyBZEF+CAMeaQMSYL+Ay4vUiZ+4CvjTG/AhhjzpSirlJVUi2XWrx9yzv0b9KfNwIDWJS+F+b2gv0rnB2aUoU4MkE0Ao4WeH3M/l5BrQB/EVkrIj+JyIhS1AVARMaKyDYR2Xb27NlyCl0px3JzcWNqn6kMbjaY6XW8mRcQCJ8MhxWvQm729VegVAVw5CWexd3jseg5ta5AJ6A/4An8ICKbS1jXetOYecA8sE5zLXO0SlUwN5sbb0S/gavNlXcO/R/Z4X14bONM5MgPMHwB1Gns7BBVDefIFsQxoOA3PAQ4UUyZpcaYDGNMIrAeiChhXaWqPFebK6/3fJ3bW9zO3PMJzOx+H+ZMnNXlFL/E2eGpGs6RCWIr0FJEQkXEHbgH+E+RMouBaBFxFREvoCsQV8K6SlULLjYXJveczPBWw/nw1AamRT+I8W8Cn90LS1+EnCxnh6hqKId1MRljckTkcWAZ4AIsMMbsFpFH7cvnGmPiRGQpsBPIA+YbY3YBFFfXUbEq5Ww2sfFqt1dxs7nxcfynZHf4Iy+EdMO2+V04utnqcvJv5uwwVQ2jU20oVYkYY5i+bTof7fmI4a2G84pvBLb/2Odvun02hA91boCq2tGpNpSqIkSEZ6KeYXT70Xy17yteTdlG7iNrILAFfPEnWPIc5Fx0dpiqhtCJ6pWqZESE8R3H425z570d75Gdl83rI/8P1zWvww+z4eiPMHyhlTSUciBNEEpVQiLCuMhxuNpcmbV9Fjl5Obwx8A3cmvWCbx6F9/vA0JnQ7k5nh6qqMe1iUqoSG9NhDM9GPcvyI8t5du2zZN80AB7dAPXawFcPwXdPQvZ5Z4epqilNEEpVcg+2fZAXurzA6qOreXLtk1z0qQejlkDPCfDTQpg/ABL3OztMVQ1pglCqCri/zf280u0V1h9bz/jV4zlvcmDgZLj/K0g/aXU57fjc2WGqakYThFJVxB9b/5HJPSbzw4kfeHzV42RmZ0LLgVaXU4MI+GYsLP5fyMp0dqiqmtAEoVQVMqzlMF7v9TrbTm9j3MpxZGRngG9DePA76P0cbP8EPrgFzsQ7O1RVDWiCUKqKua3FbbwZ/SY7zu5g7IqxpGelg4sr3PIy/OlryEyEeX2tZKHUDdAEoVQVNCR0CNP7TGdP0h7GLB9D6sVUa0GLW6wup5AoWPyYdUrsxXPODVZVWZoglKqi+jftz9t932Zfyj5GLx9NyoUUa4FPMIxYDH1fhJ2fwwf94LROZaZKTxOEUlVYn8Z9eOeWd0hITeChZQ+ReD7RWmBzgb4TYcR/4EKqNS7x0yKoRnOvKcfTBKFUFdezUU9m95/NsfRjPLTsIc5mFrizYmg0PLoRmvaA7ybAvx+GC2nOC1ZVKZoglKoGujXoxpwBcziVcYpRy0ZxKuPU5YW168L9/4ZbXoHd38C8PnByh/OCVVWGJgilqomo4CjmDZxH0vkkRi4dyfFzxy8vtNmg97Mw8v8g+4J19fWWD7TLSV2TJgilqpHIepHMGziPtKw0Ri0dxdG0o4ULNO1hneXUvC8seRa+fNAao1CqGJoglKpm2tdtz/xB88nMyWTkspEcTj1cuIB3INz7uTVVR/z/wdxoOP6TU2JVlZsmCKWqofDAcD4c9CE5eTmMWjaKg78dLFzAZrMm+xv1PZg8+HAwbJ6jXU6qEE0QSlVTrQNas2DwAgAeWvYQ+1L2XVmocRd4ZL01p9PSF+Cz+yEzuYIjVZWVJgilqrEWdVqwcPBCXG2uPLzsYeKS4q4s5BUA9/wLhrwB+5fD+73h6NaKD1ZVOpoglKrmmvk1Y9HgRXi6evLw8of55ewvVxYSgW7j4OFlIDZYOAQ2zoK8vIoPWFUamiCUqgEa+zZm0ZBF+Lr7MmbFGGLPxBZfsFEnq8up9e9gxSvw6T2QkVShsarKQxOEUjVEw9oNWTRkEUGeQYxdMZZtp7YVX9CzDvzxY/jdNDi0Bub2giM/VGisqnLQBKFUDRLsHczCwQsJ9g5m3MpxbD65ufiCItBlDIxeCW4esOj3EDNdu5xqGE0QStUwdb3qsmDwAkJ8Qnh81eNsPL7x6oUbRMDYddD2Dlg1GT65E86dvXp5Va1oglCqBgryDGLB4AWE+oXyxOonWHd03dULe/jCnR/C/7wNRzZZXU4JMRUWq3IeTRBK1VD+Hv7MHzSfVv6teHLtk6w6surqhUUgahSMXgW1fODjobD2TcjLrbiAVYXTBKFUDeZXy48PBn1AeGA4z6x7hqUJS69dIbgdjF0L7f8Ia/8K/7gD0k9XRKjKCTRBKFXD+bj7MG/gPCLqRjAxZiLfHfzu2hVq1YZhc+H2d60L6ub2hINrKiZYVaE0QSil8HbzZs6AOUTVj+KlDS/xzf5vrl1BBDo+AGPXgFcg/GMYrP4L5OZUTMCqQmiCUEoB4OXmxez+s+nesDuvbnqVL/Z+cf1K9drAmDXQ8X5YP9Uam0g74fhgVYXQBKGUyufp6smsW2bRO6Q3UzZP4ZO4T65fyd3L6m4a9j6ciLXOctq/0uGxKsfTBKGUKqSWSy3e7vs2tzS+hTe2vMFHuz8qWcWIe6wBbJ8G1vUSK16D3GyHxqocy6EJQkSGiMheETkgIi8Us7yviKSKSKz98WqBZU+JyG4R2SUin4qIhyNjVUpd5ubixrS+0xjUdBDTtk3jg50flKxi3VbW1dedRsHGt60rsH87et1qqnJyWIIQERfgXeBWIBy4V0TCiykaY4yJtD8m2+s2AsYDUcaYdoALcI+jYlVKXcnN5sabvd/k981/z6zts3gv9j1MSW4o5OYJt71tXVx3eg+8Hw17v3d4vKr8ObIF0QU4YIw5ZIzJAj4Dbi9FfVfAU0RcAS9AR76UqmCuNlde7/k6t7e4nTk75jBr+6ySJQmA9sPhkXXg19iaFXbZS5CT5diAVblyZIJoBBRsWx6zv1dUdxHZISLfi0hbAGPMcWAa8CtwEkg1xiwvbiMiMlZEtonItrNndY4Ypcqbi82FyT0nM7zVcOb/Mp/p26aXPEkEtoCHV0CXsfDDbOs+EymHHRqvKj+OTBBSzHtFv1U/A02NMRHAO8C3ACLij9XaCAUaAt4i8kBxGzHGzDPGRBljourWrVtesSulCrCJjVe7vcq9Yffy0Z6PeGPLGyVPEm4e8Lup1hTiiQdgbm+Iu87FeKpScGSCOAY0LvA6hCLdRMaYNGPMOfvzJYCbiAQBA4AEY8xZY0w28DXQw4GxKqWuQ0T4f13+HyPCR/Cv+H8xZfMU8kwppv8Ov93qcgpsAZ8/AEueh5yLjgtY3TBHJoitQEsRCRURd6xB5v8ULCAiwSIi9udd7PEkYXUtdRMRL/vy/kAxN9NVSlUkEeHZqGcZ3X40X+77ktc2vUZuaSbsCwiFh5ZBt8dgy/vw4SBIPuS4gNUNcXXUio0xOSLyOLAM6yykBcaY3SLyqH35XGA4ME5EcoDzwD3Garf+KCJfYXVB5QDbgXmOilUpVXIiwviO43GzuTFnxxyy87L5S8+/4Gor4eHE1R2G/A2aRcO346wup6GzoN0fHBu4KjUpcT9iFRAVFWW2bbvKbRSVUuXug50fMGv7LAY3G8zfov+Gm82tdCv47Vf46iE4thWiHoLBf7VOk1UVRkR+MsZEFbfMYS0IpVT1N6bDGNxsbkz/aTo5eTlM7T0VN5dSJIk6TWDU97B6Cmycac0Oe9dCCGrpuKBVielUG0qpGzKy3Uhe6PICq35dxVNrn+JibikHnl3cYOBkuO9LSDsO7/eBnSWYKFA5nCYIpdQNu7/N/bzS7RXWHVvHhNUTuJBzofQraTUIHt0ADTrA12Ng8eOQlVn+waoS0wShlCoXf2z9Ryb3mMymE5t4fNXjZGaX4eDu1wge/C9EPwvb/wkf3AJn4ss/WFUi100QIlJfRD4Uke/tr8NF5GHHh6aUqmqGtRzG671eZ+vprYxbOY6M7IzSr8TFFfq/Ag/8GzLOwgf9YHsJph1X5a4kLYhFWKeqNrS/3gc86aB4lFJV3G0tbuPN6DfZcXYHj6x4hPSs9LKt6Kb+MG4jNOoEix+Dbx6Fi+fKN1h1TSVJEEHGmC+APLCubwBKcWWMUqqmGRI6hGl9prE7aTdjl48l9WJq2VbkEwwjFkOfF2DHZ1Zr4vTu8g1WXVVJEkSGiARin0dJRLoBZfzXVkrVFAOaDmBG3xnsTdnL6OWjSbmQUrYV2Vyg3/+zEsWFVGtc4qePoBpdw1VZlSRBPI01RUYLEdkIfAw84dColFLVQt/GfZl1yywSUhN4ePnDJJ1PKvvKmvexznJq0h2+Gw//Hg0Xy9h9pUrkugnCGPMz0AdrsrxHgLbGmJ2ODkwpVT30atSL2f1nczTtKA8te4izmTcwLX/tevDA13DLy7D7a3i/N5zcUX7BqkJKchbTCOA+oBNwM9ad4UY4OjClVPXRrUE33hvwHiczTjJq2ShOZZwq+8psNuj9nHU6bPZ5mD8QtnygXU4OUJIups4FHtHAJGCoA2NSSlVDnYM7M2/gPBLPJzJq6ShOnLvBm0Q262l1OYX2hiXPwpcPWmMUqtyUpIvpiQKPMUBHwN3xoSmlqpvIepF8MPADUrNSGbl0JEfTj16/0rV4B8F9X8CAP0Pcf60up+M/l0+wqkxXUmcCOpOWUqpM2tdtz/xB88nMyWTk0pEcSTtyYyu02aDXk9akf7k51j0mNs/RLqdyUJIxiO9E5D/2x3+BvcBix4emlKquwgPD+XDQh+Tk5TBy6UgO/VYONw1q0hUejYGbBsDSF6y71p0v46m1CijB/SBEpE+BlznAEWPMMYdGVUZ6PwilqpaDvx1k9PLR5Jk8Phj0Aa38W934So2Bze/BitfApwEMXwCNO9/4equpa90PoiRjEOsKPDZW1uSglKp6WtRpwcLBC3G1ufLwsoeJSyqHOwuLQPf/tW5tKsDCIbBxFuSV4v7ZCrhGghCRdBFJK+aRLiJpFRmkUqr6aubXjEWDF+Hp6snDyx9mV+Ku8llxSCd4JAZa3worXoFP74HM5PJZdw1x1QRhjPExxvgW8/AxxvhWZJBKqeqtsW9jFg5ZiK+7L2OWjyH2TGz5rNizDvzxH3DrVDi0Bub2giM/lM+6a4ASn8UkIvVEpMmlhyODUkrVPI1qN2LRkEUEegbyv6v+l6NpN3gK7CUi0HUsPLwCXNxh0e8hZrp2OZVASc5iGioi+4EEYB1wGPjewXEppWqgYO9g5gyYg4gwfs34st106GoaRsIj6yH8dlg1GT4ZDuduYNqPGqAkLYgpQDdgnzEmFOgPbHRoVEqpGquxT2Om9p7KodRDvLzxZa53pmWpePhaZzX9zww4vMHqcjq8ofzWX82UJEFkG2OSAJuI2Iwxa4BIx4allKrJujfsztOdnmbFkRV88MsH5btyEYh6CMasglq14aPbYO2bkKe3uSmqJAniNxGpDcQAn4jITKzrIZRSymFGhI/g981/z+zts1l3dF35byC4PYxdB+3vgrV/hX8Mg/TT5b+dKqwkCWI9UAeYACwFDgK3OTAmpZRCRJjUfRJhAWG8EPMCCakJ5b+RWrVh2PswdDYc3WJ1OR1cU/7bqaJKkiAE657Ua4HawOf2LiellHIoD1cPZvabibuLO+NXjy/7/a2vRQRu/hOMXQOe/lZLYvVfrHmdariSXEn9Z2NMW+B/gYbAOhFZ6fDIlFIKaFC7AdP7TOdY+jFejHmRPOOg01PrtbGSROT9sH4qfDwU0m5wSvIqrjSzuZ4BTgFJQD3HhKOUUleKCo7i+S7Ps/bYWt6Lfc9xG3L3hjvetbqdTsRaXU77a+7v4ZJcBzFORNYCq4AgYIwxpoOjA1NKqYLuaX0Pw24axvs732flEQcftCPugbFroXYwfHInrJwEudmO3WYlVJIWRFPgSWNMW2PMa8aYPY4OSimlihIRXu72Mh2COvDihhfZn7LfsRus28o6FbbTSNgww7oCO7VmzVVakjGIF4wxsRUQi1JKXZO7izsz+s3A282bCWsmkHrRwbcYdfOE22bCnR/C6d1Wl9PepY7dZiVSljvKKaWU09TzqseMvjM4mXGS59c/T25FXODWfrg1TYdfCHx6Nyx7CXKyHL9dJ9MEoZSqciLrRfJy15fZdGITM7fPrJiNBraAh1dC5zHww2xYeCuk3ODtUis5hyYIERkiIntF5ICIvFDM8r4ikioisfbHqwWW1RGRr0QkXkTiRKS7I2NVSlUtd7a6k7tb383CXQv5PqGC5g9184DfT4O7PoLEffB+NMR9VzHbdgKHJQgRcQHeBW4FwoF7RSS8mKIxxphI+2NygfdnAkuNMWFABFAOt5pSSlUnEztP5OZ6N/PqxleJT46vuA23vcPqcgpobt37esnzkHOx4rZfQRzZgugCHDDGHDLGZAGfAbeXpKKI+AK9gQ8BjDFZxpjfHBWoUqpqcnNxY3rf6fjV8mPC6gmkXEipuI0HhFq3Ne32GGx5Hz4cBMmHKm77FcCRCaIRUPCOH8fs7xXVXUR2iMj3ItLW/l5z4CywUES2i8h8EfF2YKxKqSoqyDOImf1mkng+kWfXPUt2XgVer+BaC4b8De75F6QkwPt9YPc3Fbd9B3NkgpBi3is6sfvPQFNjTATwDvCt/X1X4GZgjjGmI5ABXDGGASAiY0Vkm4hsO3tWb/6hVE3UNqgtr/V4jS2ntvDWtrcqPoCw38OjG6Bua/hyJPz3aci+UPFxlDNHJohjQOMCr0OAQhObGGPSjDHn7M+XAG4iEmSve8wY86O96FdYCeMKxph5xpgoY0xU3bp1y/szKKWqiKEthvJAmwf4Z9w/WXxgccUHUKcJjPoeejwB2z6E+QMg8UDFx1GOHJkgtgItRSRURNyBe4D/FCwgIsEiIvbnXezxJBljTgFHRaS1vWh/QK/gVkpd0zNRz9A1uCuTf5jMrsRdFR+AixsM+gvc9wWkHYd5fWDnlxUfRzlxWIIwxuQAj2NNFR4HfGGM2S0ij4rIo/Ziw4FdIrIDmAXcYy7fX/AJrBsU7cS6g91fHRWrUqp6cLW5MrXPVOp61WXCmgkknk90TiCtBltdTsHt4evRsPhxyCrH+2tXECnX+706WVRUlNm2bZuzw1BKOdne5L08sOQB2gS24cNBH+Lm4uacQHJzYM3rsOEtqBcOdy2yxikqERH5yRgTVdwyvZJaKVXttA5ozZSeU9h+ZjtvbHnDeYG4uMKA1+CBf8O5MzCvL8T+y3nxlJImCKVUtTQkdAgPtXuIL/Z9wZf7nDwOcNMAq8upUSf4dhx88yhkZTg3phLQBKGUqrbGdxxPz0Y9+euPfyX2TKxzg/FtACMWQ5+JsOMzqzVxerdzY7oOTRBKqWrLxebCm9Fv0tC7IU+tfYrTGaedG5DNBfq9aCWK87/BB7fATx9BJR0L1gShlKrW/Gr5MbPfTDKzM3lq7VNczK0EcyY17wPjNkKTbvDdePh6DFxMd3ZUV9AEoZSq9m7yv4m/9vorvyT+wpQfplApzt6sXQ8e+Br6vQy7/m1N03Fyp7OjKkQThFKqRujftD+PRjzK4oOL+TT+U2eHY7G5QJ/n4MHvIDvTuvp66/xK0+WkCUIpVWOMixhH38Z9+fvWv7P11FZnh3NZs17WWU6h0fB/z1jzOV1w8O1US0AThFKqxrCJjb/1+htNfJvwzNpnOHHuxPUrVRTvILjvSxgwyboJ0fu94fjPTg1JE4RSqkap7V6bWf1mkZ2XzZNrnuR8znlnh3SZzQa9noJRSyA327rHxOa5Tuty0gShlKpxmvk1483ebxKfHM+kTZMqx6B1QU26WV1ON/WHpROtu9adr8CbIdlpglBK1Ui9Q3rzRMcnWJKwhI/3fOzscK7kFQD3fgaDXod9S2FubzhWsXPNaYJQStVYo9uPZmDTgbz101tsOrHJ2eFcSQR6PG7d2hRgwWDY9E6FdTlpglBK1Vgiwl96/oUWdVrw3LrnOJp29PqVnCEkCh5dD62GwPKX4dN7IDPZ4ZvVBKGUqtG83LyY2W8mAOPXjCczu5Let8HTH+7+J9z6dzi4Gub2gl83O3STmiCUUjVeY5/GTO0zlUOph3h548uVb9D6EhHo+gg8vNy6e93C30HMW5CX55DNaYJQSimgR8MePN3paVYcWcH8X+Y7O5xra9gRHlkP4UNh1Z/hk+EOmT7ctdzXqJRSVdSI8BHEJcfxzvZ3aB3Qmt4hvZ0d0tV5+MHwhdAsGo5sBDevct+E3nJUKaUKuJBzgRHfj+Bo+lH+9ft/EeoX6uyQrs8Yq/upDPSWo0opVUIerh7M7DcTdxd3xq8eT3pW5ZuG+wplTA7XowlCKaWKaFC7AdP6TONo+lFejHmRPOOYQeDKThOEUkoVo3NwZ57v/Dxrj61lzo45zg7HKXSQWimlruLesHuJT45n7o65tPZvzYCmA5wdUoXSFoRSSl2FiPBSt5foENSBFze8yP6U/c4OqUJpglBKqWuo5VKLt/q+hbebNxPWTCD1ovNv5FNRNEEopdR11Peuz4y+MziZcZKJ6yeSm5fr7JAqhCYIpZQqgch6kbzU9SU2ntjIrO2znB1OhdBBaqWUKqHhrYYTnxzPgl0LCAsI49bQW50dkkNpC0IppUphYueJ3FzvZl7d+CrxyfHODsehqv1UG9nZ2Rw7dowLFy44Kaqay8PDg5CQENzc3JwdilLlKvF8Inf/925cxZXP/ucz/D38nR1SmV1rqo1qnyASEhLw8fEhMDAQcdDl6OpKxhiSkpJIT08nNLQKzGWjVCntStzFg98/SGS9SN4f+D6utqrZY1+j52K6cOGCJgcnEBECAwO15aaqrXZB7Xitx2tsObWF6dumOzsch6iaKa+UNDk4h+53Vd0NbTGUuKQ4/hn3T9oEtmFoi6HODqlcObQFISJDRGSviBwQkReKWd5XRFJFJNb+eLXIchcR2S4i/3VknI5Wu3btK96bNGkSjRo1IjIykvDwcD799FMnRKaUulHPRD1D1+Cu/HnTn9mVuMvZ4ZQrhyUIEXEB3gVuBcKBe0UkvJiiMcaYSPtjcpFlE4A4R8XobE899RSxsbEsXryYRx55hOzsbKfFYowhz0G3LVSqOnO1uTK1z1SCPIOYsGYCiecTnR1SuXFkC6ILcMAYc8gYkwV8Btxe0soiEgL8Hqjk9/67cS1btsTLy4uUlJQrln333Xd07dqVjh07MmDAAE6fPg3AuXPnGDVqFO3bt6dDhw78+9//BmDp0qXcfPPNRERE0L9/f8BqrUybNi1/ne3atePw4cMcPnyYNm3a8Nhjj3HzzTdz9OhRxo0bR1RUFG3btuW1117Lr7N161Z69OhBREQEXbp0IT09nejoaGJjY/PL9OzZk507dzpiFylVqfl7+DPzlpmkXUzjmbXPkJ3rvB975cmRYxCNgKMFXh8DuhZTrruI7ABOAM8aY3bb338beB7wKa+A/vzdbvacSCuv1QEQ3tCX125re0Pr+Pnnn2nZsiX16tW7YlmvXr3YvHkzIsL8+fP5+9//zvTp05kyZQp+fn788ssvAKSkpHD27FnGjBnD+vXrCQ0NJTk5+brb3rt3LwsXLuS9994D4PXXXycgIIDc3Fz69+/Pzp07CQsL4+677+bzzz+nc+fOpKWl4enpyejRo1m0aBFvv/02+/bt4+LFi3To0OGG9oVSVVVYQBhTek7hufXP8caWN3il+yvODumGOTJBFDdCWfSc2p+BpsaYcyLyO+BboKWI/A9wxhjzk4j0veZGRMYCYwGaNGlyozFXqBkzZvDBBx9w6NAhli5dWmyZY8eOcffdd3Py5EmysrLyTxlduXIln332WX45f39/vvvuO3r37p1fJiAg4LoxNG3alG7duuW//uKLL5g3bx45OTmcPHmSPXv2ICI0aNCAzp07A+Dr6wvAXXfdxZQpU5g6dSoLFixg5MiRZdoPSlUXQ0KHEJccZ11pHRjGXa3ucnZIN8SRCeIY0LjA6xCsVkI+Y0xagedLROQ9EQkCegJD7UnDA/AVkX8aYx4ouhFjzDxgHljXQVwroBv9pV/ennrqKZ599lm+/vprRowYwcGDB/Hw8ChU5oknnuDpp59m6NChrF27lkmTJgHWmEHRs4SKew/A1dW10PhCwVNPvb29858nJCQwbdo0tm7dir+/PyNHjuTChQtXXa+XlxcDBw5k8eLFfPHFF+j9wJWC8R3Hszd5L3/98a+0rNOSyHqRzg6pzBw5BrEVqzUQKiLuwD3AfwoWEJFgsR95RKSLPZ4kY8z/M8aEGGOa2eutLi45VBd/+MMfiIqK4qOPPrpiWWpqKo0aNQIotHzQoEHMnj07/3VKSgrdu3dn3bp1JCQkAOR3MTVr1oyff/4ZsLqzLi0vKi0tDW9vb/z8/Dh9+jTff/89AGFhYZw4cYKtW7cCkJ6eTk5ODgCjR49m/PjxdO7cuUQtFqWqOxebC2/2fpMG3g14au1TnM447eyQysxhCcIYkwM8DizDOhPpC2PMbhF5VEQetRcbDuyyj0HMAu4x1enSbrvMzExCQkLyH2+99dYVZV599VXeeuutK84kmjRpEnfddRfR0dEEBQXlv//yyy+TkpJCu3btiIiIYM2aNdStW5d58+bxhz/8gYiICO6++24A7rzzTpKTk4mMjGTOnDm0atWq2DgjIiLo2LEjbdu25aGHHqJnz54AuLu78/nnn/PEE08QERHBwIED81shnTp1wtfXl1GjRpXLvlKqOvCr5cesfrPIzM7kqbVPcTH3orNDKpNqP9VGXFwcbdq0cVJE1d+JEyfo27cv8fHx2GxX/t7Q/a9qslVHVvHk2ie546Y7mNxjcqW8eLRGT7WhHOfjjz+ma9euvP7668UmB6Vquv5N+/NoxKN8e+BbPo2vehfD1oipNpRjjBgxghEjRjg7DKUqtXER44hPiufvW/9OS/+WdA7u7OyQSkx/9imllAPZxMbfov9GE98mPLP2GU6eO+nskEpME4RSSjlYbffazOw3k+y8bCasmcD5nPPODqlENEEopVQFCPUL5c3ebxKfHM+kTZOoCicIaYJQSqkK0jukN090fIIlCUv4eM/Hzg7nujRBVIDKON13s2bNSExMvGp8SinHGN1+NAObDuStn95i04lNzg7nmjRBOFFpp/u+dPWyUqrqEhH+0vMvNPdrznPrnuNo+tHrV3ISTRCVwLWm+x45ciRPP/00/fr1Y+LEiRw8eJAhQ4bQqVMnoqOjiY+PB+D06dMMGzaMiIgIIiIi2LTJ+mVyxx130KlTJ9q2bcu8efMq9HMppYrn5ebFrH6zABi/ejyZ2ZlOjqh4Nes6iO9fgFO/lO86g9vDrW/c0CquNd03wL59+1i5ciUuLi7079+fuXPn0rJlS3788Ucee+wxVq9ezfjx4+nTpw/ffPMNubm5nDt3DoAFCxYQEBDA+fPn6dy5M3feeSeBgYE3FK9S6sY19m3M1D5TGbdyHC9vfJnpfaZXuiuta1aCqGRKMt03WNNqu7i4cO7cOTZt2sRdd12eQvjiRWuOl9WrV/Pxx9agl4uLC35+fgDMmjWLb775BoCjR4+yf/9+TRBKVRI9Gvbg6U5PM23bNOb/Mp8xHcY4O6RCalaCuMFf+uWtJNN9w+UpufPy8qhTp06hu7hdy9q1a1m5ciU//PADXl5e9O3bt9BU30op5xsRPoI9SXt4Z/s7tA5oTe+Q3s4OKZ+OQVQC15ruuyBfX19CQ0P58ssvAev+Dzt27ACgf//+zJkzB4Dc3FzS0tJITU3F398fLy8v4uPj2bx5s2M/iFKq1ESEST0mERYQxsT1E0lILX46fmfQBFEBbmS676I++eQTPvzwQyIiImjbti2LFy8GYObMmaxZs4b27dvTqVMndu/ezZAhQ8jJyaFDhw688sorhe4cp5SqPDxdPXm739u42dyYsGYC57LOOTskQKf7Vg6m+1+pktt6aitjlo8hulE0M2+ZiU0c/xtep/tWSqkqoHNwZ57v/Dxrj61lzo45zg5HE4RSSlUm94bdyx033cHcHXNZdWSVU2PRBKGUUpWIiPByt5dpH9SeFze8yIGUA06LRROEUkpVMrVcajGj7wy83LyYsGYCqRdTnRKHJgillKqE6nvXZ0bfGZzIOMHE9RPJzcut8Bg0QSilVCUVWS+Sl7q+xMYTG5m1fVaFb18TRAW4kem+z549S9euXenYsSMxMTG89NJLNG7cWKfoVqqGGN5qOH9s9UcW7FrA0oSrT8njCJognKgk032vWrWKsLAwtm/fTnR0NLfddhtbtmyp0Dhzcyu+aauUuuyFLi/QsV5HXtn4CvHJ8RW2XU0QlcDVpvuOjY3l+eefZ8mSJURGRnL+/Hm6detGgwYNrrm+3NxcRo4cSbt27Wjfvj0zZswA4MCBAwwYMICIiAhuvvlmDh48iDGG5557Lr/s559/DljzOPXr14/77ruP9u3bk5uby3PPPUfnzp3p0KED77//vmN2hlLqCm4ubrzV9y18a/kyYfUEUi5ceWsAR6hRk/W9ueXNcs++YQFhTOwy8YbWcbXpviMjI5k8eTLbtm1j9uzZJV5fbGwsx48fZ9euXQD89ttvANx///288MILDBs2jAsXLpCXl8fXX39NbGwsO3bsIDExkc6dO9O7tzVZ2JYtW9i1axehoaHMmzcPPz8/tm7dysWLF+nZsyeDBg0iNDT0hj67UqpkgjyDmNlvJg9+/yDPrnuW9we+j6vNsYdwbUE40YwZM2jdujVdu3Zl0qRJ5bbe5s2bc+jQIZ544gmWLl2Kr68v6enpHD9+nGHDhgHg4eGBl5cXGzZs4N5778XFxYX69evTp08ftm7dCkCXLl3yE8Dy5cv5+OOPiYyMpGvXriQlJbF///5yi1kpdX3tgtrxWo/X2HJqC9O3TXf49mpUC+JGf+mXt5JO9309ubm5dOrUCYChQ4cyefJkduzYwbJly3j33Xf54osvePvtt4ute625uC5NM36p3DvvvMPgwYNLHZ9SqvwMbTGUuKQ4/hn3T9oEtmFoi6EO25a2ICqBkk73fTUuLi7ExsYSGxvL5MmTSUxMJC8vjzvvvJMpU6bw888/4+vrS0hICN9++y1g3WgoMzOT3r178/nnn5Obm8vZs2dZv349Xbp0uWIbgwcPZs6cOfkD6fv27SMjI6PMn1kpVXZPRz1Nl+Au/HnTn9mVuMth29EEUQHKc7rv559/npCQkPx1Ftc1dfz4cfr27UtkZCQjR47kb3/7GwD/+Mc/mDVrFh06dKBHjx6cOnWKYcOG0aFDByIiIrjlllv4+9//TnBw8BXrHD16NOHh4dx88820a9eORx55hJycnLLtEKXUDXGzuTGtzzSCPIOYsGYCiecTHbIdne5bOZTuf6UcJz45nj8t+RPhgeHMHzQfNxe3Uq9Dp/tWSqlqKCwgjMk9JxPqF4qh/H/s16hBaqWUqm5uDb2VW0Nvdci6tQWhlFKqWDUiQVSncZaqRPe7UlWbQxOEiAwRkb0ickBEXihmeV8RSRWRWPvjVfv7jUVkjYjEichuEZlQ1hg8PDxISkrSg1UFM8aQlJRUpus6lFKVg8PGIETEBXgXGAgcA7aKyH+MMXuKFI0xxvxPkfdygGeMMT+LiA/wk4isKKbudYWEhHDs2DHOnj1blo+hboCHhwchISHODkMpVUaOHKTuAhwwxhwCEJHPgNuB6x7kjTEngZP25+kiEgc0Kkndotzc3HS+IKWUKgNHdjE1Ao4WeH3M/l5R3UVkh4h8LyJtiy4UkWZAR+DH4jYiImNFZJuIbNNWglJKlR9HJggp5r2iAwE/A02NMRHAO8C3hVYgUhv4N/CkMSatuI0YY+YZY6KMMVF169a98aiVUkoBjk0Qx4DGBV6HACcKFjDGpBljztmfLwHcRCQIQETcsJLDJ8aYrx0Yp1JKqWI4bKoNEXEF9gH9gePAVuA+Y8zuAmWCgdPGGCMiXYCvgKb2xR8BycaYJ0uxzbPAkTKGHAQ4ZkKTG6NxlY7GVToaV+lUx7iaGmOK7X5x2CC1MSZHRB4HlgEuwAJjzG4RedS+fC4wHBgnIjnAeeAee7LoBfwJ+EVEYu2rfNHeyrjWNsvcxyQi2642H4kzaVylo3GVjsZVOjUtLodOtWE/oC8p8t7cAs9nA1fcKs0Ys4HixzCUUkpVkBpxJbVSSqnS0wRx2TxnB3AVGlfpaFylo3GVTo2Kq1rdD0IppVT50RaEUkqpYlX7BHG9CQMLlOssIrkiMry0dZ0Q12ER+cU+weG2q9V1RFxXm2CxNJ/JCXE5bX8ViC3WPvHkutLUdVJcDttfJYlNRJ4r8O+4y/79Dyjp53JSXM78P+knIt+JNSPFbhEZVdK612WMqbYPrNNrDwLNAXdgBxB+lXKrsc64Gl6auhUdl/39w0CQM/YX0Bf4b1k/U0XHVQn2Vx2sOcSa2F/XqyT7q9i4HLm/yvK5gduA1ZVhn10trkrwHXsReNP+vC6QbC97w/ururcg8icMNMZkAZcmDCzqCayrts+UoW5Fx+VIN/KZK8P+qmglies+4GtjzK8AxpgzpajrjLgcrbSf+17g0zLWrai4HKkkcRnAR0QEqI2VIHJKWPeaqnuCuO6EgSLSCBgGzKWwkk42WNFxgfWFWC4iP4nI2HKKqURx2RU3waJT99c14gLn7q9WgL+IrLVvf0Qp6jojLnDc/ippbACIiBcwBOtHUqnqVnBc4Nzv2GygDdZURr8AE4wxeSWse03V/Z7UJZkw8G1gojEm10rAparrjLgAehpjTohIPWCFiMQbY9ZXUFyXJlg8JyK/w5pgsWUJ6zojLnDu/nIFOmFNOeMJ/CAim0tYt8LjMsbsw3H7q6SxXXIbsNEYk1yGuqV1I3GBc79jg4FY4BaghX37MSWse03VvQVx3QkDgSjgMxE5jDX1x3sickcJ6zojLowxJ+x/zwDfYDUlKyQuc/UJFp26v64Rl1P3l73MUmNMhjEmEVgPRJSwrjPicuT+Kmlsl9xD4W4cZ++zq8Xl7O/YKKzuQmOMOQAkAGElrHtt5T2oUpkeWL+SDgGhXB6kaXuN8ou4PEhdqroVGJc34FPg+SZgSEXFBQRz+fqZLsCvWL9UnLq/rhGXs/dXG2CVvawXsAtoVwn219Xictj+Ks13H/DD6kv3Luv/mwqMy9nfsTnAJPvz+liTowaVx/6q1l1MpmQTBpaqrrPjwvoCfGPvdnIF/mWMWVqBcRU7wSLg7P11tYkfnbq/jDFxIrIU2AnkAfONMbsAnLm/rhaXiDTHQfurpLHZiw4DlhtjMq5X19lx4fz/k1OARSLyC9aPoonGahXe8HdMr6RWSilVrOo+BqGUUqqMNEEopZQqliYIpZRSxdIEoZRSqliaIJRSShVLE4RSdiJSR0Qesz/vKyL/dcA2FkmBmXlLUL6ZiOy6yrK1IlLp7o+sqg9NEEpdVgd4rDQVRMTFMaEo5XyaIJS67A2ghYjEAlOB2iLylYjEi8gn9tkyL839/6qIbADuEpFBIvKDiPwsIl+KSG17uTdEZI+I7BSRaQW201tENonIoUutCbFMFes+A7+IyN1FgxMRTxH5zL6+z7HmUFLKYar1ldRKldILQDtjTKSI9AUWA22x5q/ZCPQENtjLXjDG9LLP9/Q1MMAYkyEiE4GnRWQ21lW3YfYruusU2E4DoBfWfDn/Ab4C/gBEYs2HFARsFZGik72NAzKNMR1EpAPWBIVKOYy2IJS6ui3GmGPGmjo5FmhWYNnn9r/dgHBgo73l8SDQFEgDLgDzReQPQGaBut8aY/KMMXuwpmkAK2F8aozJNcacBtYBnYvE0xv4J4AxZifWNBlKOYy2IJS6uosFnudS+P/Lpbl4BFhhjLm3aGUR6YI1nfY9wONY0zEXXa8U+Xs9OjeOqjDaglDqsnTAp5R1NgM9ReQmsG4mIyKt7OMQfsaaevxJrO6ja1kP3C0iLiJSF6u1sKWYMvfbt9MO6FDKWJUqFW1BKGVnjEkSkY3200rPA6dLUOesiIwEPhWRWva3X8ZKNotFxAOrdfDUdVb1DdAda0pmAzxvjDklIs0KlJkDLBSRnVhdXkUTiFLlSmdzVUopVSztYlJKKVUsTRBKKaWKpQlCKaVUsTRBKKWUKpYmCKWUUsXSBKGUUqpYmiCUUkoVSxOEUkqpYv1/fDHubZiLqtQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ee+yxQOG/c+dOJk2axAMPPED37t3Zv39/nRhKSkrIyLDu07d1bgaPx8Ptt9/OY4891uQ2n376KdnZ2W06rooCxsAH/2ElhLFXa0KIQtqjOQREhNdff52PPvqIQYMGMXHiRBYsWMBvf/tbbr75ZhYtWsTkyZP5+uuv6drVGhysf//+zJ49m9GjR3PVVVcxduxYAMrKyrjooosYPXo006dP59FHHwXgzjvvZNSoUYwcOZIzzzyTMWPG1InhF7/4Bffccw/Tpk1rtHmpJdddd12DGsRLL71ETk4Oo0ePZv369dx3330ncnpUR2UM/ON++OR3MG4BXPy4JoQoJMY0eMCnw5gwYYKpf5N2y5YtDBs2zKGIFOi/QVQyBt67D1Y+DhN+ABf+F7j0mrKjEpG1QR2K69A7Q0qp5hkDf/8lrPojnPZDuPB3II09Qa6igSYFpVTTjIF37oHVT8Kkm+CChzUhRDlNCkqpxhkDb98Jnz8Nk2+B8x/UhNAJaFJQSjXk98PbP4fcP8PU2+A7/64JoZPQpKCUqsvvt+ZCWLcIpv0Uzr1fE0InoklBKVXL74c3fwzrF8MZP4ez79OE0MnoM2Uh4Ha7ycnJYcyYMXUGvXNC8CB5zz33HLfeeisAf/rTn/jLX/4CwMKFC8nIyKCyshKA/Px8MjMzAdizZw9dunQJ/D7Bg+upKOP3wYpbrYQw/S5NCJ2UJoUQ6NKlCxs2bGDjxo089NBD3HPPPa3e1xhTZ7iLULnpppuYP39+4LPb7ebPf/5zo9tmZ2cHfp8FCxa0OIqq6oD8Pnj9ZtjwAsy4B866VxNCJ6VJIcRKS0vrDDH9yCOPcNpppzF69Gh+/etfA9bV+LBhw7j55psZN24cn3zyCcOGDeP6669nxIgRnHfeeRw/fhywRjidPHkyo0eP5vLLL6eoqAiAGTNmBEZbDb7Sb8r999/P7373u8Dnn/70pzz66KMNejG39PuoKODzwvIb4YulcNa/wQwdz6ozi+p7Ct/8539SuaV9h86OGzaU3vfe2+w2x48fJycnh4qKCg4dOsQHH3wAwLvvvsv27dtZs2YNxhguueQSPv74YwYMGMC2bdt49tlneeKJJ9izZw/bt29nyZIlPP3008yePZtXX32Vq6++mvnz5/P4448zffp0fvWrX/Gb3/ym2TGKWmvAgAGcfvrpLF68uMGQ2Dt37iQnJ4eysjLKy8tZvXr1SX+fihA+Lyy/Ab56Fc75lXUfQXVqWlMIgZrmo61bt/LOO+8wf/58jDG8++67vPvuu4wdO5Zx48axdetWtm/fDlgD4k2ePDlwjEGDBpGTkwPA+PHj2bNnDyUlJRQXFzN9+nQAFixYwMcff9xucd9777088sgjDZqvapqPdu7cyWOPPcYNN9zQxBFUh+KrhlevsxLCdx7QhNCBVFT7OFJWGZJjR3VNoaUr+nCYMmUK+fn5HDlyBGMM99xzDzfeeGOdbfbs2RMYGK9G/eGta5qPmhI89HbNsNttdcopp5CTk8OyZcua3OaSSy7h2muvPaHjqwjirYJXfwBb3oTzHoSptzodkWpGRbWP9fuKWb27gFW7Cli3r5iLRvXh93Ny2v27ojopRIKtW7fi8/lIT0/n/PPP57777uOqq66iW7duHDhwAI+n9VMXJicnk5qayieffMIZZ5zB4sWLA7WGzMxM1q5dy8SJE3nllVdOON5f/vKXfO9732tyvQ6ZHQW8VfDyQtj2Vzj/IZhys9MRqXoqqn2s21fE6l2FrNpVwPr9xVR5/bgERvRNZv7kgZw1tGfLBzoBmhRCoOaeAlhPEy1atAi32815553Hli1bmDJlCgDdunXj+eefx+1u/fDDixYt4qabbqK8vJysrCyeffZZwJpbefbs2SxevJizzz77hGMfMWIE48aNY926dYFlNfcUjDHExsbyzDPPnPDxlcO8lbBsAXz9N/juIzBJmwIjQU0SWGUngQ37iqnyWUlgZEYyC6YMZHJWOhMy00juEto5sHXobNXu9N8gQlVXwLJrYPu78L3/skY8VY44XuVj/b4iVu0qYNWuQjbsr00CozKSmZSVzuSsNCZkppEU3/5JQIfOVqqzq66Al66CHf+Aix6DCXpfKJyOV9XUBKx7Ahv2F1PtM4EkcO20TLsmkEpiCJJAW2hSUCraVR+HJfNg14dw8f/A+AVORxT1yqu8rNtbHEgCG/OsJOB2CSMzkvnB6YOsJDDQ+SRQX1QmBWMMor0xHdGRmyOjUlU5LJkLuz+25lMee7XTEUWl8iova/fWNgd9EZQERmUkc93pWYHmoG5xkV3sRnZ0JyA+Pp6CggLS09M1MYSZMYaCggLi4+OdDkUBVB2DF+fAnk/hsichZ57TEUWNY5XBSaCAL/JK8PqtJDC6XzI/PCOLyVnpjB+YGvFJoL6OFW0r9OvXj7y8PI4cOeJ0KJ1SfHw8/fr1czoMVXnUSgj7VsLMp2D0bKcj6tCOVXrJDUoCX9pJIMZOAjecWZsEunawJFBfx46+ER6Ph0GDBjkdhlLOqSyDF2bB/tUw82kYdYXTEXU4Ryu95O4pZNWuQlbvtmoCPjsJjOmfwo3Ta5NAQmx0FaPR9dso1dlVlMILV0BeLnz//2DkTKcj6hCOVnr5fI/VR2D1rkK+PGAlAY9bGNMvhR9Nz2ZSVlpUJoH6ovu3U6ozqSiB578PB9fDrGdh+KVORxSxyiqqyd1TxKrd1o3hr4KSQE7/FG6ekc2kQemMG5gS9UmgvpD/tiLiBnKBA8aYi0QkDXgJyAT2ALONMUX2tvcA1wE+4MfGmL+HOj6losLxYnh+JhzaCLOeg2EXt7RHp1JaUU3unsLAsBFfHijBb8DjFsb2T+XmGdlMzkpn3IBUusS2foSBaBSOFPgTYAuQZH++G3jfGPOwiNxtf75LRIYDc4ERQF/gHyIyxBjjC0OMSnVcx4tg8eXwzVcwezEMvdDpiBxXWlHN57sLWb3bSgJf2Ukg1u0iZ0AKt551CpOz0hmrSaCBkCYFEekHfA94EPiZvfhSYIb9fhHwIXCXvXypMaYS2C0iO4CJwGehjFGpDq28EBZfBoe3wJzn4dQLnI7IESXHq+0bw1Zz0KaD9ZLA2YOZnJXGuAGpxHs0CTQn1DWFx4BfAIlBy3oZYw4BGGMOiUjNUH8ZwKqg7fLsZXWIyA3ADWBNDKNUp1VeCH+5BI58DXNegCHnOR1R2JQct2oCq3YVsGp3AZsOlmIMxMa4GNs/hdvOHmzXBFI0CbRRyJKCiFwEHDbGrBWRGa3ZpZFlDbrHGmOeAp4Ca0C8k4lRqQ7rWD785VIo2AHzXoRTznU6opAqKa9mTaAmUMDmQ7VJYNyAFH5yjpUEcvprEjhZoawpTAMuEZELgXggSUSeB74VkT52LaEPcNjePg/oH7R/P+BgCONTqmM6esSqIRTugnlLIfsspyNqd8XlVazZXRgYSnrLN1YSiItxMW5AKj89ZwiTs9IYo0mg3YVl6Gy7pnCH/fTRI0BB0I3mNGPML0RkBPAi1n2EvsD7wODmbjQ3NnS2UlHt6GFYdDEU7YUrX4Ks6U5H1C6Ky6sCN4VX7Spka1ASGD8wlclZ6UzOSmdM/2TiYjQJnKxIGzr7YWCZiFwH7ANmARhjNonIMmAz4AVu0SePlApS9o2VEEry4KqXYdAZTkd0wo5Wevl8dyErd+azcmdtc1C8x0oCPzt3CJOz0xndT5NAuEXdJDtKRaXSQ7DoIuv16ldg4FSnI2qTimof6/YWsXJnASt35rPRHjYi1u1i3MAUpmZ3Z4omgbCJtJqCUqotSg5YNYSj38I1r8GAyU5H1KJqn58v8kr4zK4J5O4tosrrD4wietP0LKZmd2f8QH1ENNJoUlAqkpXkwXMXWU8bXf0aDJjkdESN8vsNmw+V8pldE1izu5BjVVbr7/A+ScyfPJCpp6RzWmZaxE0qo+rSpKBUpCreZyWE40Uw/3Xo12ht3xHGGHYeOWo1B+2w+goUl1cDkN2jKzPH9WNqdjqTstJJ6xrrcLSqLTQpKBWJivZa9xAqSqyEkDHe6YjYX1geuDG8cmcBR8oqAchI6cJ5w3sF7gv0StJJljqyViUFEekKHDfG+EVkCDAU+Jsxpjqk0SnVGRXutu4hVJbB/Deg71hHwvi2tCLQHLRyZwF5RccB6JEYx9TsdPunO/3TEhyJT4VGa2sKHwNniEgqVv+BXGAOcFWoAlOqUyrYaSWE6nJYsAL6jAnbVxcdq2LVroLAE0I7jxwDILmLh8lZaVx/RhZTs9M5pWc3neo2irU2KYgxptzuW/C4Meb/icj6UAamVKdTsNO6h+CtgAVvQu9RIf26sopqPt9TyModViKo6TWcEOtm4qA05pzWn6nZ3RnWJwm3S5NAOBhjqPZXU+mrpNJXSZWvqs5rzfsqXxXdu3RnVI/2/xtpdVIQkSlYNYPr2rivUqol+duthOD3wsK3oNeIdv+KJvsKxLgYP8DqMDb1lHRG90vB43a1+/d3BMYYqvyNF8TNLWtpfaWvkkp/w2WNbd9aF2RewCPTH2n3c9Dagv2nwD3AcrvncRbwz3aPRqnO6Mg2q8nI+K2E0HNYuxzW6itQHKgJrN1X21dgTL9kfjQ9m6nZ6YyLoL4CfuNvVcHZ6DJ/7bJqX9NX280dr8pfddK/g8flIc4dR6w7ljh3XJ33se5Yunm6ERvfyDpXbJ3tGts3eFlqXGo7nPGGWpUUjDEfAR8Ffd4F/DgkESnVmRzeYiUEBBa8BT2HnvChfH7DlkOlgRvDa3YXUh7UV2DBlIFMze7OaYPS6BYXg8/vo8pfRZX/GEePV1Htq7Y++6qo8lufq/3VgSvhmnVev7fO5wbb2strjhd8jOBtAuuDvrfaf/LPrsS6YpstTBNjE0l3p7dY+AYX0s0dr/6rS06uluWvqsJfWoqvtAx/WfBrAb6yUvylZfjKSokfOgzmzjnp81Vfa58+mgDcizWFZmAfY8zodo9IqShjjLEKUn+9gvHwZqrfuJkqTwxV332Yal8xVXkfN1841yy3j5N/rJxDJWV8W3aMgvJyvP5qEC9dYg3pQyDDY4iJ8eM11fzjWDVvr6+ieq11LF87Di3mEhexrlg8bk+gMI11x+JxefC4PIHPXWK64HEHLXPVbtdS4dtYAVx/H4/Lc9KF8snyV1ZahXpZWeDVV1qKv6yskYK+zFpeVhYo8E1lC01IMTG4ExMhRDf7W9t89AJwJ/Al4A9JJKrT8xs/Xr/X+jHWq8/va/jZWK/V/mprmfEFXqv91YF9apZX+6trt7H3D3xP0HY131P/O+t8h/29wd8ZfKzg/YML8iald7FeV93X6vPklhgwMfh8bvx+N5gYYlweErvFkRQXT2pCEl1ja690awrrBgVxzbJ6hXjwa/C2sa7YOgV88DZuV2Q0P50sYwymsjKoEK9XmJcdbVCo1169W0nAVLXQBOXx4E5MxJ2YiCspCXdiIjF9+tifE3EnJgVe3UmJuIJfE7shXbqE9Omv1iaFI8aYFSGLQjmi2ldNubecY9XHKK8u55jXei2vLq9dHrQ+uDBsrICtX7gGF+qBAju4cK5X8JqGcyqFXIwrhhiJIcYVg9vlDryv+XGLO/DqcXkC28VJnLV9M/t7XJ66V841hXPZt8SueoJYVxyxM+4hNmUAMa6YOoVzzbbFx/xs3F/G2t1lrN5VxsHiKkDokRjHdO0r0IAxBlNRYRXaR5u/Qq9fmAcK9eoWmrA8Htx2YV5TqHv69m28EE9KxNUtsc5niY+P6Ed6W5sUfi0iz2D1UQjUbYwxr4UkKtWAMYbj3uOUe61Cu06BbS+rv/y493ijBX7N+9a23wpCl5guxLpj6xSUNQVioHAMKiDjJC5QUHpcnjr7tGb/Ots0sk+gEA4qsOt8h8uNRzx1tqkpqGsKb5e4wv+f8+B6eONeiO0GC9+EtKw6qwsDfQWs+wK7gvoKTMlK56bpViLI7hGdfQXqFOpNFuKNF+Y+uxmGFgp18XhwJSfXXpknJRHbL6NhYV5zNZ9obVPzKnFxUXnua7Q2KVyL1YvZQ23zkQE0KTTB6/c2WVjXKbCDrsTrX6HXL8RbeyXtcXno6ulKQkwCCR7rp2tMV3p06VFnec37rp6udPF0oWtM1zrLEzwJJMQk0CUmtNXVTuPAWlh8OcQlWwkhNZOyimrW7C4MDB2x5VApUNtXYG4U9BXwHzuGt6AAb34BvkLr1VuQj6+gAG9BofU+vwCfXbC3WKjHxdU2ryQm4k5JIbZ//3pNL7WFefAVvSspCVdcXJh+846ptUlhjDEmtD1pHGSModJXWbcwDm5WCVremivxcm95m543rl9IJ3gSSI9PZ0DiAKvAjulirbML95qCvqZAD7zayzxuHYUy4uTlwuKZ+LuksG7GYv65poKVO//FF/X6Cvz8O5HfV8AYg7+kBG9hId58u3DPL8BbWIAvv8BKAHZB7y0sxBw/3uhxXMnJxKSnE5OeTtzQobhTkptvT7ev3rVQD63WJoVVIjLcGLM5pNGEybbCbdz58Z1WwV5tNcm09kmMGIlpcJWd4EkgLT6ttmBupsCuv298TLzjT0uo0Kny+tm57gOy/j6fYpKYfeRO9i7Ni7i+Asbnw1dUZF/R5+MrLLSu7Avy7Sv7AvvK3iroG72ad7lwp6XZBX0asQMGWu+7p+NOs1/T04np3p2Y1FQkVkdPjUStTQqnAwtEZDfWPQUBTEd9JLWrpytDUofUNp0EX4nXazqpf4Ue69Y/ZNU0r8/PpoOlgTGEvHtW8id5mIMmiV+nPcR5406t01cglPxVVYEmmkYL96CreV9REfgbPlgoHo9VkKen4+7RnbihQ4lJT7OXda8t6NPTcaekIO7oeAqpM2vVdJwiMrCx5caYve0eURvodJzKaV6fn6/sJLBqVwG5e4o4WukF4LLUPfy28t/xJvSi+uoVpPRu9L9Rm/jLy2uv5uu1ydct8Avwl5Y2egxJSAg029QU6MGFu7XcKvBdiYl6PykKnfR0nMaYvSJyOjDYGPOsiPQAurVnkEp1BNU+P18dKGHVrkI7CdTOMJbdoyuX5vRlclY6p8dsJfX1ByGtP3EL3oTE3o0ezxiDv7S09mq+IPhGbKFV0OfnB5ptTHl5o8dxJSXVts+feipd09Jwd6+9mg9OAK4EfXxVNa21PZp/DUwATgWexXoK6XlgWuhCU8p51T4/Xx4osWsChawNSgKn9OzG5eMymDQonUlZafRMtCaXMbs+xffnq6nw9MU39H68H+bWPm1Tv8AvKGj8uXiXC3dqql2Yp9Glf471vqagT08LXM3HpKVp+7xqN61t1LwcGAusAzDGHBSRxJBFpZRDaiacX7WrgDXbv2X79gPEHCsjsfo4Q7r4uSNJOCXRR/8YL3GlR/F9VILvjWKOlpRQWlyCr7gIf/lxIBk4BkvuqD24x0OMfSPW3T2duCFD6hbuNc026Wm4U1O1fV45orVJocoYY0TEQGAmNqU6BH9VFb7iYvwlJfhqfopL8BUXU1VUzJGDhyn8Jp/j+YX4S0vpWnmMydXHOdvb9GPFx91uqpKTcds/nh49cQ/ojXv/e7i6eHDPuIWYjKzap23S03ElJWn7vIp4rU0Ky0Tkf4EUEbke+AHwTOjCUqohf1UVvqJifCX1C/hiq5AP/hy0vql2eACvuDgam0ClJwF/10Ti+vQmoWd30jN60K1HmtXzNTkZd0oK7uQU61n65GRc3er1KC7aA3/+LmQAP3gT0rNDfj6UCoXW3mj+nYh8ByjFuq/wK2PMeyGNTEUtf2VlUEFuFeD+4MK8uInCvYlOUIA1cmRKSu2Ve58+xA8bBklJHCaO3ZUuNh8VNpYaClzxlHkS6NO/JzlD+jI5uzsTB6WR3u0EO0WVHoK/XGpNoXnt25oQVIfW2hvNvzXG3AW818iypvaJx5rbOc7+nleMMb8WkTTgJaxhuPcAs40xRfY+92DN7OYDfmyM+fuJ/FIqPPwVFfUK8uJAYe4PbqapV8CbioqmD+rxBK7G3ckpeDIyiB8xIlDYB9YFJQBXcgqurgmICJVen3VPYGcBq3YXsHZvERXV1vP3Q/slMjkrnclZaUwclE5a13a4OXusABZfBsfyYf6KkMyYplQ4tbafwjpjzLh6y75orvOaWHXrrsaYoyLiAT4FfgLMBAqNMQ+LyN1AqjHmLhEZDiwBJgJ9gX8AQ4xpuqux9lNoH8bns8adKSq2r+CL6r4vLq69sg8u3JsZ9108HqvgTkm2m2Bqml5Sggr4lKAEYP1IQkKb2t0rvT427i8J9BNYu7eISq+VBIb1SWLSoDQmZ6UzaVAaqe2RBIJVlMCiS+DIVrjqFRh0RvseX6kQOeF+CiLyI+BmIEtEvghalQj8q7l9jZVtjtofPfaPAS4FZtjLFwEfAnfZy5caYyqB3SKyAytBfNbc96i6THV1UCFejLeoqLZAr3lfFFTQFxXhKy2Fpi4O7Cv3GLtN3TNwAPGBq/SmC/hQjfle6fWxYV9xoJ/Aun1WEhCBob2TuHLSACZnpTMxMwRJIFhVObw4B779CuYu0YSgokZLzUcvAn8DHgLuDlpeZowpbOngIuIG1gKnAH80xqwWkV7GmEMAxphDItLT3jwDWBW0e569rP4xbwBuABgwYEBLIXRogeaZegW5t06hXne9/+jRJo8n8fFW4Z2aijslmfi+w+zCPAV3Siru1IbvXV27OvrETEW1jw37iwM1gfX7igNJYFjvJK6aNNBuDkojJSFMz+p7K+Glq2H/avj+/8GQ88LzvUqFQUtJwRhj9ojILfVXiEhaS4nBbvrJEZEUYLmIjGxm88ZKngaXr8aYp4CnwGo+au77I4UxBv+x8sav0ovrNtN4gwr65m6surp2tQt3q/COzcy0C3zrc0yg8E8J/Li6dAnjb31iKqp9rN8XlAT2F1NlJ4HhfZK4evLAQE0gOcGB0WB9Xnj1h7DzfbjkDzByZvhjUCqEWlNTuAjrat9Qt+A2QFZjO9VnjCkWkQ+BC4BvRaSPXUvoAxy2N8sD+gft1g842Jrjh5Px+62ZnOxCPbgQb67Qb3I2JxFrzHe74Pb07EX8kFODruhT6hb2qalW80yU9GCtqPaxbl9RoDloQ1ASGNE3ifl2EjjNqSQQzO+HFbfBlhVw/kMw7hpn41EqBJpNCsaYi+zXQW09sD0+UrWdELoA5wK/BVYAC4CH7dc37F1WAC+KyO+xbjQPBta09Xvbwni99g3WhgV5o23x9k9jo0kC4HbXKcQ9AwcQP2a0VZAHltd7n5TUqXquVlT7WLe3yKoJ7C5kw75iqnx+XAIj+iazYIqVBCZkppHcJYLmhTAG3rkLNr4IM+6FKTc7HZFSIdHmsXtF5H5jzP2t2LQPsMi+r+AClhlj3hKRz7A6w10H7ANmARhjNonIMmAz4AVuae7Jo5Nx/Msv2XfdD5scRRLsp2dqCvDUVOIGDw5qc09ptKDXESUbOl5l1QRW22MHbdhfmwRGZiSzcFomk7PSmJCZRlJ8BCWB+j74d1jzFEy5Fab/wulolAqZVj2SWmeHRh5PdcqJPpJa/c03FDz9TMOr91S7LT4lpc2PRirL8Sofa/cWsXp3QaA5qNpncAmMyki2+wmkMz4zNbKTQLBPH4V/3A/jFsDF/w36d6E6uJMeOrv+8U4yHsd5evem933/5nQYUaG8ysu6vbU3hjfmWUnA7RJGZiTzg9MHWc1BA1NJ7ChJINiap62EMPIKuOhRTQgq6p1IUoiIWoJyRnmVl7U19wR2FbJxfzFev5UERmUkc93pWUzKSuu4SSDYxqXw9h0w5Ltw+Z/A1Xnu/ajOq7XDXGQB/w1MAfz2fYHbjTG7Qhmccl5Nc9DKnfms2lXAF3klgSQwul8y15+ZxaRB1j2BUE8vGVZb3oTXb4ZBZ8Ks58DdwROcUq3U2v/FLwJ/xJpXAWAu1pAUk0IRlHJOldfPxrxiVu4oYOXOfNbbTwfF2EnghjOzmGQ3B3WNpiQQbOcH8MoPIGOc1VvZE+90REqFTWv/V4sxZnHQ5+dF5NZQBKTCy+c3bDpYwsqd1kTzn+8u5Hi1L9BPYOG0TKZkW/0Eoqom0JR9q2DpVdB9CFz1MsTprLOqc2nt//J/2oPXLcXqtDYH+Ks94imtGfJCRQZjDNsPH2XljnxW7rRuDpdWWBPND+7ZjdkT+jEluzuTs8I4bESkOLgBXpgFSX3hmuXQJdXpiJQKu9YmhTn26431lv+ANvRsVuFnjGFfYXmgJvDZznzyj1YBMCAtgQtH9WFKdjpTstMDcwx3Ske2wfMzIT4Z5r8B3Xq2vI9SUai1k+y0uUezcs43JRWs3JlvJ4ECDhRbYyj1TIzjjME9rCSQlU7/tASHI40QRXusSXLEbSWE5H5OR6SUY1r79JEH+BFwpr3oQ+B/jTFNDOijwqngaCWrdhWycmc+n+0sYFf+MQBSEzxMyU7nphnZTM1OJ6u7syOeRqTArGnHddY0pWh989GTWPMhPGF/vsZe9sNQBKWaV1pRzZpdhXaTUD5bvykDoGusm0lZ6Vw5aQBTstMZ1jsJl0uTQJN01jSlGmhtUjjNGDMm6PMHIrIxFAGphoL7CvxrZwFf5hXjNxAX42JCZip3nn8qU7LTGZWRjMftcjrcjqGixLqHULTHmjWt33inI1IqIrQ2KfhEJNsYsxMCndlCMlidar6vQE7/FG496xSmZHdn7IAU4j3ay7bNdNY0pZrU2qRwB9Zjqbuwxj4aCFwbsqg6meb6Cozsm8y1QX0ForbDWLjorGlKNavFEsYe+noM1vwGp2Ilha32XMrqBBhj+Prbo4EnhFbtKqBM+wqEns6aplSLWkwKxhifiFxijHkU+CIMMUUdYwx7C8oDN4ZX7Sqo01fge9pXIPR01jSlWqW1bRErReQPwEvAsZqFxph1IYkqChwqOc5ngQ5jtX0FeiVpX4GwC5417axf6qxpSjWjtUlhqv36G/tVsHoyn93uEXVQ2lcggtXMmjb1NjjzTqejUSqiNZsURORn9tu3sJJAcGnWtinbokxTfQW6xcUwaVAaV04awNTs7gztnah9BZz06aPwyX/B+IXwnX/XSXKUakFLNYVE+/VU4DTgDazEcDHwcQjjijjHq3zk7i0MPCHUVF+B0RnJxGhfgcgQPGva936vCUGpVmg2KRhjfgMgIu8C44wxZfbn+4GXQx6dg6q8fjbsLw48IbR+XxHVPqN9BToKnTVNqRPS2nsKA4CqoM9VQGa7R+Og4L4C/9qRT+6eojp9BX4wbZD2FegodNY0pU5Ya0u3xcAaEVmOdS/hcmBRyKIKg+b6Cgzp1Y05p/VnSnY6kwelk5yghUqHobOmKXVSWjt09oMi8jegZjyAa40x60MXVmit31fE9X/JDfQVGJiewEWj+wQ6jGlfgQ4qMGvaqTprmlInqNXtIHafhKjol5CZ3pXTT+nO1FO6MzU7nX6p2legw6sza9prOmuaUieoUzaOp3aN5bG5Y50OQ7UXnTVNqXYTsmcnRaS/iPxTRLaIyCYR+Ym9PE1E3hOR7fZratA+94jIDhHZJiLnhyo2FUVqZk1zxeisaUq1g1A+UO8Ffm6MGQZMBm4RkeHA3cD7xpjBwPv2Z+x1c4ERwAXAE/ZgfEo1rmbWNG8FXPO6zpqmVDsIWVIwxhyqGRvJ7t+wBcgALqX2yaVFwGX2+0uBpcaYSmPMbmAHMDFU8akOLnjWtKtfhV7DnY5IqagQlq63IpIJjAVWA72MMYfAShxATQNwBrA/aLc8e1n9Y90gIrkiknvkyJGQxq0iVPCsafOWQobOmqZUewl5UhCRbsCrwE+NMaXNbdrIsgbjKxljnjLGTDDGTOjRo0d7hak6iuBZ02Yv1lnTlGpnIU0KIuLBSggvGGNesxd/KyJ97PV9gMP28jygf9Du/YCDoYxPdTDeSnjpKnvWtGd01jSlQiCUTx8J8H/AFmPM74NWrQAW2O8XYA2yV7N8rojEicggrJne1oQqPtXB+Lzw6nVWj+WL/wdGXO50REpFpVD2U5gGXAN8KSIb7GX3Ag8Dy0TkOmAfMAvAGLNJRJYBm7GeXLrFGOMLYXyqowjMmvYmXPCwzpqmVAiFLCkYYz6l8fsEAOc0sc+DwIOhikl1QPVnTZv8I6cjUiqq6cD/KrLprGlKhZUmBRW5Pvm9zpqmVJhpUlCRac3T8P5vYNQsnTVNqTDSpKAiT82saadeCJc9qbOmKRVGmhRUZAmeNe2KZ3XWNKXCTJOCihw73tdZ05RymCYFFRl01jSlIoImBeW8mlnTkvvBNct11jSlHKRJQTmrzqxpr0M3HeRQKSdpUlDO0VnTlIo4nXKOZhUBSg/CokusWdMWvq2zpikVITQpqPA7VgB/uQzKC2DBCp01TakIoklBhVfNrGnFe61pNHXWNKUiiiYFFT7Bs6bNXQKZpzsdkVKqHk0KKjyCZ0274s86a5pSEUqTggq94FnTLvmDzpqmVATTR1JVaPn9sOJWnTVNqQ5Ck4IKncCsaUvgrH/TWdOU6gA0KajQqTNr2h1OR6OUagVNCio0dNY0pTokTQqq/emsaUp1WJoUVPvSWdOU6tA0Kaj2E5g1bbrOmqZUB6VJQbWPwKxp42HuizprmlIdlCYFdfL2fhY0a9oynTVNqQ4sZElBRP4sIodF5KugZWki8p6IbLdfU4PW3SMiO0Rkm4icH6q4VDs7uAFenK2zpikVJUJZU3gOuKDesruB940xg4H37c+IyHBgLjDC3ucJEdE7lJEuMGtais6aplSUCFlSMMZ8DBTWW3wpsMh+vwi4LGj5UmNMpTFmN7ADmBiq2FQ7qDNr2us6a5pSUSLc9xR6GWMOAdivPe3lGcD+oO3y7GUNiMgNIpIrIrlHjhwJabCqCcGzpl3zus6aplQUiZQbzY31bjKNbWiMecoYM8EYM6FHD22uCLvgWdOuflVnTVMqyoQ7KXwrIn0A7NfD9vI8oH/Qdv2Ag2GOTbWkogSev9yaNe3Kl3TWNKWiULiTwgpggf1+AfBG0PK5IhInIoOAwcCaMMemmhOYNW0zzHleZ01TKkqFbJIdEVkCzAC6i0ge8GvgYWCZiFwH7ANmARhjNonIMmAz4AVuMcb4QhWbagNjIC8XPnigdta0wd9xOiqlVIiELCkYY+Y1seqcJrZ/EHgwVPGoNjAGDq6HTa/BptehZD+443TWNKU6AZ2OU1mMgW++tBPBcuuRU5cHss+Gs34JQy+E+GSno1RKhZgmhc7MGDi82UoCX70GhTtB3JA1A868E4Z+T3soK9XJaFLojI5ss5LApuWQvw3EBYPOhGk/hqEXQ9d0pyNUSjlEk0Jnkb/DSgKbXrNqB4j1BNGkG2DYpTpEhVIK0KQQ3Qp3194j+OZLa9mAKfDdR2D4JZDY29n4lFIRR5NCtCneZ9cIlltPEAH0mwjnPwQjLoOkvo6Gp5SKbJoUokFJHmx+w7pPcCDXWtZ3HJz3HzD8UkgZ4Gx8SqkOQ5NCR1V6yEoEm5bD/lXWst6j4dz7YfhlkDbIyeiUUh2UJoWO5Ojh2kSwdyVgoNdIOPvfYMRMHa1UKXXSNClEumMFsMVOBHs+BeOHHkNhxj3WPYIepzodoVIqimhSiETlhbD1LSsR7PoIjA/ST4Ez7oCRM6HnMKcjVEpFKU0KkeJ4MWx727pZvOuf4PdCaiZM+4mVCHqNBGls2gmllGo/mhScVFEK2/5m1Qh2vg++KkgeAFNusQae65OjiUApFVaaFMKt8ih8/Y6VCLa/B75KSMqAiTdYiSBjvCYCpZRjNCmEQ1U5bH/X6l389bvgPQ7desOEa62nhvqdBq5ImRlVKdWZaVIIleoK2PGedY/g63eguhy69oSxV1s1ggFTNBEopSKOJoX25K2EnR9YiWDb36CqDBLSYfQc62bxwGngcjsdpVJKNUmTwsnyVsHuj6xEsPWvUFkC8SlWH4KRMyHzTHDraVZKdQxaWp0IXzXs/ti6WbzlTagohrhkGHaR1TSUNQPcHqejVEqpNtOk0Fp+n9WjeNNrsHkFHC+E2ERrmsoRMyH7LIiJczpKpZQ6KZoUmuP3wb7PrBrB5jfg2BHwdIVTL7ASwSnngife6SiVUqrdaFKoz++HvDXWPYLNb8DRbyCmCww532oaGnwexCY4HaVSSoWEJgWwJrDPy7VrBK9D6QFwx8Hg71g3iwefD3HdnI5SKaVCrvMmBWOsmck2LYdNr0PJPnDHWk1C594PQy6A+CSno1RKqbCKuKQgIhcA/w24gWeMMQ+3+5ccWAevXAtFe8AVA9lnw1n3wqnfhS4p7f51SinVUURUUhARN/BH4DtAHvC5iKwwxmxu1y9Kzawdinro9yAhrV0Pr5RSHVVEJQVgIrDDGLMLQESWApcC7ZsUEtLg6lfb9ZBKKRUNIm3wnQxgf9DnPHuZUkqpMIi0pNDYmNGmzgYiN4hIrojkHjlyJExhKaVU5xBpSSEP6B/0uR9wMHgDY8xTxpgJxpgJPXr0CGtwSikV7SItKXwODBaRQSISC8wFVjgck1JKdRoRdaPZGOMVkVuBv2M9kvpnY8wmh8NSSqlOI6KSAoAx5m3gbafjUEqpzijSmo+UUko5SJOCUkqpADHGtLxVhBKRI8DekzhEdyC/ncJpTxpX22hcbaNxtU00xjXQGNPo45sdOimcLBHJNcZMcDqO+jSuttG42kbjapvOFpc2HymllArQpKCUUiqgsyeFp5wOoAkaV9toXG2jcbVNp4qrU99TUEopVVdnrykopZQKoklBKaVUQFQmBRG5QES2icgOEbm7me1OExGfiFzR1n0diGuPiHwpIhtEJDeccYnIDBEpsb97g4j8qq2/kwNxOXa+gmLbICKbROSjtuzrUFxO/n3dGfRv+JX9t5/W2t/JobicPF/JIvKmiGy0/x2vbe2+rWKMiaofrIH0dgJZQCywERjexHYfYI2zdEVb9g13XPbyPUB3J84XMAN460R/p3DHFQHnKwVrtsAB9ueeEXK+Go3L6fNVb/uLgQ8i4Xw1FZfT5wu4F/it/b4HUGhv2y7nKxprCoEpPY0xVUDNlJ713Qa8Chw+gX3DHVconczvHAnnK9xaE9eVwGvGmH0AxpjDbdjXibhCqa2/8zxgyQnuG664Qqk1cRkgUUQE6IaVFLyt3LdF0ZgUWpzSU0QygMuBP7V1X4fiAusP4V0RWSsiN7RTTK2KyzbFrq7+TURGtHHfcMcFzp6vIUCqiHxof//8NuzrRFzg/N8XIpIAXIB1UdSmfcMcFzh7vv4ADMOagOxL4CfGGH8r921RxA2d3Q5anNITeAy4yxjjs5Jtm/Z1Ii6AacaYgyLSE3hPRLYaYz4OU1zrsMZKOSoiFwKvA4Nbua8TcYGz5ysGGA+cA3QBPhORVa3cN+xxGWO+xtnzVeNi4F/GmMIT2LetTiYucPZ8nQ9sAM4Gsu3v/6SV+7YoGmsKLU7pCUwAlorIHuAK4AkRuayV+zoRF8aYg/brYWA5VlUxLHEZY0qNMUft928DHhHp3pp9HYrL0fNlb/OOMeaYMSYf+BgY08p9nYjL6fNVYy51m2icPl9NxeX0+boWqxnQGGN2ALuBoa3ct2XtfaPE6R+sq6FdwCBqb7aMaGb756i90dymfcMYV1cgMej9SuCCcMUF9Ka2o+NEYB/WVYmj56uZuJw+X8OA9+1tE4CvgJERcL6aisvR82Vvl4zVNt71RP/PhDEup/++ngTut9/3Ag5gjZjaLucr6pqPTBNTeorITfb6xtrrm93X6biw/uGX201KMcCLxph3whjXFcCPRMQLHAfmGusv0unz1WhcIuLo+TLGbBGRd4AvAD/wjDHmKwAnz1dTcYlIFs7+fYF1L+1dY8yxlvZ1Oi6c///478BzIvIl1kXQXcaq+bXL35cOc6GUUiogGu8pKKWUOkGaFJRSSgVoUlBKKRWgSUEppVSAJgWllFIBmhRUpyUiKSJys/1+hoi8FYLveE6CRrttxfaZIvJVE+s+FJGIm0BeRRdNCqozSwFubssOIuIOTShKRQZNCqozexjIFpENwCNANxF5RUS2isgL9iiUNWPn/0pEPgVmich5IvKZiKwTkZdFpJu93cMisllEvhCR3wV9z5kislJEdtXUGsTyiFjj9H8pInPqByciXURkqX28l7DGK1IqpKKuR7NSbXA3MNIYkyMiM4A3gBFY48X8C5gGfGpvW2GMOd0eW+k14FxjzDERuQv4mYj8Aav361C7V3VK0Pf0AU7HGp9mBfAKMBPIwRp7qDvwuYjUH1DtR0C5MWa0iIzGGgBQqZDSmoJStdYYY/KMNQzxBiAzaN1L9utkYDjwL7uGsQAYCJQCFcAzIjITKA/a93VjjN8YsxlriASwksQSY4zPGPMt8BFwWr14zgSeBzDGfIE1PIVSIaU1BaVqVQa991H3/0fN2DcCvGeMmVd/ZxGZiDUs9VzgVqyhjesfV+q9tkTHoVFhpTUF1ZmVAYlt3GcVME1ETgFrAhYRGWLfV0g21hDeP8VqGmrOx8AcEXGLSA+sWsGaRra5yv6ekcDoNsaqVJtpTUF1WsaYAhH5l/0I6HHg21bsc0REFgJLRCTOXvxvWAnmDRGJx6oF3N7CoZYDU7CGNzbAL4wx34hIZtA2TwLPisgXWM1Z9ZOGUu1OR0lVSikVoM1HSimlAjQpKKWUCtCkoJRSKkCTglJKqQBNCkoppQI0KSillArQpKCUUirg/wOkQtztFtBW4QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid = plt.GridSpec(7, 3, wspace=0.5, hspace=0.5)\n",
    "plt.subplot(grid[0:7,0:3])\n",
    "plt.plot(thresset, lr_test_accuracy, label=\"LR accuracy\")\n",
    "plt.plot(thresset, lr_test_recall, label=\"LR recall\")\n",
    "plt.plot(thresset, lr_test_fscore, label=\"LR f1-score\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.xlabel(\"threshold\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.subplot(grid[0:7,0:3])\n",
    "plt.plot(thresset, svm_test_accuracy, label=\"SVM accuracy\")\n",
    "plt.plot(thresset, svm_test_recall, label=\"SVM recall\")\n",
    "plt.plot(thresset, svm_test_fscore, label=\"SVM f1-score\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.xlabel(\"threshold\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.subplot(grid[0:7,0:3])\n",
    "plt.plot(thresset, gnb_test_accuracy, label=\"GaussianNB accuracy\")\n",
    "plt.plot(thresset, gnb_test_recall, label=\"GaussianNB recall\")\n",
    "plt.plot(thresset, gnb_test_fscore, label=\"GaussianNB f1-score\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.xlabel(\"threshold\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.subplot(grid[0:7,0:3])\n",
    "plt.plot(thresset, bnb_test_accuracy, label=\"BernoulliNB accuracy\")\n",
    "plt.plot(thresset, bnb_test_recall, label=\"BernoulliNB recall\")\n",
    "plt.plot(thresset, bnb_test_fscore, label=\"BernoulliNB f1-score\")\n",
    "plt.ylabel(\"value\")\n",
    "plt.xlabel(\"threshold\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.subplot(grid[0:7,0:3])\n",
    "plt.plot(thresset, lr_drop_item, label=\"LR \")\n",
    "plt.plot(thresset, svm_drop_item, label=\"SVM\")\n",
    "plt.plot(thresset, gnb_drop_item, label=\"GaussianNB\")\n",
    "plt.plot(thresset, bnb_drop_item, label=\"BernoulliNB\")\n",
    "plt.ylabel(\"drop-items\")\n",
    "plt.xlabel(\"threshold\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.6181082292891338, 0.6130627091396561, 0.6144506403132312, 0.609556232488849, 0.6103966158475995]\n",
      "[0.6030150753768844, 0.5946398659966499, 0.5946398659966499, 0.5879396984924623, 0.5862646566164154]\n",
      "[0.6028658182679788, 0.5934860506590142, 0.5930531198807321, 0.585596183683394, 0.5829478749635087]\n",
      "[333, 344, 348, 354, 361]\n",
      "[0.6314344415622509, 0.6233823977208509, 0.6207711010190289, 0.6165970255108062, 0.6180307293522677]\n",
      "[0.628140703517588, 0.6164154103852596, 0.609715242881072, 0.5996649916247906, 0.5963149078726968]\n",
      "[0.6290850840958143, 0.6175328617310336, 0.6104119745429744, 0.5990088981061749, 0.5941440761909333]\n",
      "[286, 303, 319, 339, 353]\n",
      "[0.7005023703691065, 0.6245163439027496, 0.5995233082408087, 0.5805424876881633, 0.5313595002037212]\n",
      "[0.5644891122278057, 0.6013400335008375, 0.5879396984924623, 0.47738693467336685, 0.4472361809045226]\n",
      "[0.42051551526807834, 0.5406719220113204, 0.5885225277732368, 0.3778948756126311, 0.28409216411728977]\n",
      "[8, 84, 322, 544, 592]\n",
      "[0.6088371358803025, 0.6141231640952467, 0.616870873899507, 0.6104508020600108, 0.6100991506347859]\n",
      "[0.6130653266331658, 0.6130653266331658, 0.6030150753768844, 0.576214405360134, 0.5494137353433836]\n",
      "[0.6063906349767634, 0.6135102995895508, 0.6031554216136166, 0.5680859291129265, 0.5234371977179993]\n",
      "[215, 273, 329, 387, 443]\n"
     ]
    }
   ],
   "source": [
    "print(gnb_test_accuracy) \n",
    "print(gnb_test_recall) \n",
    "print(gnb_test_fscore) \n",
    "print(gnb_drop_item) \n",
    "print(bnb_test_accuracy) \n",
    "print(bnb_test_recall) \n",
    "print(bnb_test_fscore) \n",
    "print(bnb_drop_item) \n",
    "print(svm_test_accuracy) \n",
    "print(svm_test_recall) \n",
    "print(svm_test_fscore) \n",
    "print(svm_drop_item)\n",
    "print(lr_test_accuracy) \n",
    "print(lr_test_recall) \n",
    "print(lr_test_fscore)\n",
    "print(lr_drop_item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "597"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
